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Dissertations / Theses on the topic 'Ranking data'
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Vanichbuncha, Tita. "Modelling partial ranking data." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/66664/.
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Ranking is one of the most used methods in not only in statistics but also in other field such as computer science and psychology. This method helps us determine order of objects in a group such as preference of animal species, and has very broad applications. However, when the number of objects to be ranked becomes larger, the uncertainty of the ranking typically increases since it is harder for the ranker to express their preference accurately. This leads to the idea of partial ranking which allows rankers to rank just a subset of objects in the group and then combine their results together to form the global ranking. This thesis focuses on this type of data. The main challenge is how to accurately analyze partially ranked data and decide the global ranking. There are several models that address this kind of problem such as the Bradley-Terry (BT) model and the Plackett-Luce (PL) model. The BT model is for paired comparisons while the PL model is for any number of ranked objects. The PL model is slow to fit using existing R packages. We implement the algorithms in R and do empirical studies using simulated data. The results show that our algorithms perform faster than the existing packages in R. We also implement R code for computing the observed information matrix. Rank-breaking methods are also considered in order to be able to use the BT model with different weightings instead of using the PL model. We examine the performance of various weightings by experimental studies with the simulated data and with real-world data. Our BTw-Sqrt weighting performs best when the number of rankers is small. In order to choose subsets of objects to be ranked, we consider three existing criteria which are D-optimality, E-optimality, and Wald and we propose three new methods. Experiments have been done using simulated data and the results compared with random selection. Our result shows that the existing criteria sometimes perform better than random selection. Our proposed methods usually ensure that the PL model can be fitted to data from fewer rankers than random selection. We describe two extensions of the PL model, the Rank-Ordered Logit (ROL) model and the Benter model. The ROL model extends the PL model by allowing covariates to be incorporated and the Benter model allows preferences for higher-ranked items to be stronger than for lower-ranked items. Both extensions improve the fit of the PL model to an example dataset when using the Likelihood Ratio (LR) test to compare models. We combine these two extensions to give a model that incorporates covariates and allows for a dampening effect. The combined model further improves the fit to our example data when compared with the ROL model by using LR test. We implement R codes for analyzing and computing the observed information matrices of the ROL, Benter, and combined models. We also explore another type of partial ranking data where individuals are allowed to mention any objects rather than being given a predefined list of objects to rank. We consider the idea of Participatory Risk Mapping (PRM) which provides severity and incidence scores. The severity and incidence scores can be modelled using the PL model and a new proposed model, respectively.
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Lo, Siu-ming, and 盧小皿. "Factor analysis for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B30162464.
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Lo, Siu-ming. "Factor analysis for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20792967.
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林漢坤 and Hon-kwan Lam. "Analysis of ranking data with covariates." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31215476.
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Qi, Fang, and 齊放. "Some topics in modeling ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209210.
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Many applications of analysis of ranking data arise from different fields of study, such as psychology, economics, and politics. Over the past decade, many ranking data models have been proposed. AdaBoost is proved to be a very successful technique to generate a stronger classifier from weak ones; it can be viewed as a forward stagewise additive modeling using the exponential loss function. Motivated by this, a new AdaBoost algorithm is developed for ranking data. Taking into consideration the ordinal structure of the ranking data, I propose measures based on the Spearman/Kendall distance to evaluate classifier instead of the usual misclassification rate. Some ranking datasets are tested by the new algorithm, and the results show that the new algorithm outperforms traditional algorithms.
The distance-based model assumes that the probability of observing a ranking depends on the distance between the ranking and its central ranking. Prediction of ranking data can be made by combining distance-based model with the famous k-nearest-neighbor (kNN) method. This model can be improved by assigning weights to the neighbors according to their distances to the central ranking and assigning weights to the features according to their relative importance. For the feature weighting part, a revised version of the traditional ReliefF algorithm is proposed. From the experimental results we can see that the new algorithm is more suitable for ranking data problem.
Error-correcting output codes (ECOC) is widely used in solving multi-class learning problems by decomposing the multi-class problem into several binary classification problems. Several ECOCs for ranking data are proposed and tested. By combining these ECOCs and some traditional binary classifiers, a predictive model for ranking data with high accuracy can be made.
While the mixture of factor analyzers (MFA) is useful tool for analyzing heterogeneous data, it cannot be directly used for ranking data due to the special discrete ordinal structures of rankings. I fill in this gap by extending MFA to accommodate for complete and incomplete/partial ranking data. Both simulated and real examples are studied to illustrate the effectiveness of the proposed MFA methods.published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Lam, Hon-kwan. "Analysis of ranking data with covariates /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19943313.
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Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.
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Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.
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Lee, Hong, and 李匡. "Model-based decision trees for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45149707.
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Korba, Anna. "Learning from ranking data : theory and methods." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009/document.
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Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classementsRanking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages
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Gregory, Machon. "Shape identication and ranking in temporal data sets." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9319.
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Thesis (M.S.) -- University of Maryland, College Park, 2009.Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Cai, Yilun, and 蔡奕倫. "Ranking and similarity queries on complex data types." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209507.
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Ranking queries and similarity queries are elementary operations with many important applications. There are lots of research works investigating efficient evaluation of various ranking and similarity queries in databases over the past few decades. In this thesis, ranking and similarity queries on three interesting complex data types are studied, namely, multidimensional cube, object summary and tree. Efficient and effective solutions are proposed to solve their related applications.
First, the evaluation of ranking queries on multidimensional cubes is studied. In exploratory data analysis, a relation can be considered as a multidimensional cube to investigate the relationship among its attributes. Given a relation with records that can be ranked, an interesting problem is to identify selection conditions for the relation, which result in sub-relations qualified by an input record and render the ranking of the input record as high as possible among the qualifying tuples. The ranking of the input record in a sub-relation measures the quality of the corresponding multidimensional cube of this sub-relation. In this thesis, a standing maximization problem, which aims to identify a multidimensional cube of high quality, is extensively studied. As an immediate consequence of its NP-hardness, three greedy methods are proposed to explore the search space only partially, while striving to identify sub-optimal solutions of high quality.
Next, the efficient evaluation of ranking queries on object summaries is investigated. An object summary is a tree structure of tuples that summarizes the context of a particular data subject tuple. The object summary has been used as a model of keyword search in relational databases; where given a set of keywords, the objective is to identify the data subject tuples relevant to the keywords and their corresponding object summaries. However, a keyword search result may return a large number of object summaries, which brings in the issue of effectively and efficiently ranking them in order to present only the most important ones to the user. In this thesis, a model that ranks object summaries according to their relevance to a set of input thematic keywords is introduced. Efficient algorithms are proposed to answer the proposed thematic ranking query.
Finally, the similarity join query on tree-structured data is studied. Treestructured data are ubiquitous nowadays and a number of applications require efficient management of such data. Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this thesis, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph.published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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徐兆邦 and Shiu-bong Chui. "Estimation methods for rank data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222535.
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Chui, Shiu-bong. "Estimation methods for rank data /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21415110.
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Malone, Gwendolyn Joy. "Ranking and Selection Procedures for Bernoulli and Multinomial Data." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/7603.
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Ranking and Selection procedures have been designed to select the best system from a
number of alternatives, where the best system is defined by the given problem. The primary
focus of this thesis is on experiments where the data are from simulated systems. In simulation
ranking and selection procedures, four classes of comparison problems are typically
encountered. We focus on two of them: Bernoulli and multinomial selection. Therefore, we
wish to select the best system from a number of simulated alternatives where the best system
is defined as either the one with the largest probability of success (Bernoulli selection)
or the one with the greatest probability of being the best performer (multinomial selection).
We focus on procedures that are sequential and use an indifference-zone formulation
wherein the user specifies the smallest practical difference he wishes to detect between the
best system and other contenders.
We apply fully sequential procedures due to Kim and Nelson (2004) to Bernoulli data
for terminating simulations, employing common random numbers. We find that significant
savings in total observations can be realized for two to five systems when we wish to detect
small differences between competing systems. We also study the multinomial selection
problem. We offer a Monte Carlo simulation of the Bechhofer and Kulkarni (1984) MBK
multinomial procedure and provide extended tables of results. In addition, we introduce a
multi-factor extension of the MBK procedure. This procedure allows for multiple independent
factors of interest to be tested simultaneously from one data source (e.g., one person
will answer multiple independent surveys) with significant savings in total observations
compared to the factors being tested in independent experiments (each survey is run with
separate focus groups and results are combined after the experiment). Another multi-factor
multinomial procedure is also introduced, which is an extension to the MBG procedure due to Bechhofer and Goldsman (1985, 1986). This procedure performs better that any other
procedure to date for the multi-factor multinomial selection problem and should always be
used whenever table values for the truncation point are available.
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陳潔妍 and Kit-yin Chan. "Bayesian analysis of wandering vector models for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214939.
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Chen, Li. "Ranking-Based Methods for Gene Selection in Microarray Data." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3888.
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DNA microarrays have been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. One of the major goals of microarray data analysis is the detection of differentially expressed genes across two kinds of tissue samples or samples obtained under two experimental conditions. A large number of gene detection methods have been developed and most of them are based on statistical analysis. However the statistical analysis methods have the limitations due to the small sample size and unknown distribution and error structure of microarray data. In this thesis, a study of ranking-based gene selection methods which have weak assumption about the data was done. Three approaches are proposed to integrate the individual ranks to select differentially expressed genes in microarray data. The experiments are implemented on the simulated and biological microarray data, and the results show that ranking-based methods outperform the t-test and SAM in selecting differentially expressed genes, especially when the sample size is small.
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Chan, Kit-yin. "Bayesian analysis of wandering vector models for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19977025.
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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.
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Herzig, Daniel Markus [Verfasser]. "Ranking for Web Data Search Using On-The-Fly Data Integration / Daniel Markus Herzig." Karlsruhe : KIT Scientific Publishing, 2014. http://www.ksp.kit.edu.
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Hwang, Heasoo. "Dynamic link-based ranking over large-scale graph-structured data." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404629.
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Thesis (Ph. D.)--University of California, San Diego, 2010.Title from first page of PDF file (viewed June 11, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 93-97).
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Alsarem, Mazen. "Semantic snippets via query-biased ranking of linked data entities." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI044/document.
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Dans cette thèse, nous introduisons un nouvel artefact interactif pour le SERP: le "Snippet sémantique". Les snippets sémantiques s'appuient sur la coexistence des deux Webs pour faciliter le transfert des connaissances aux utilisateurs grâce a une contextualisation sémantique du besoin d'information de l'utilisateur. Ils font apparaître les relations entre le besoin d'information et les entités les plus pertinentes présentes dans la page WebIn this thesis, we introduce a new interactive artifact for the SERP: the "Semantic Snippet". Semantic Snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user's information need. It makes apparent the relationships between the information need and the most relevant entities present in the web page
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Stojkovic, Ivan. "Functional Norm Regularization for Margin-Based Ranking on Temporal Data." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/522550.
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Computer and Information SciencePh.D.
Quantifying the properties of interest is an important problem in many domains, e.g., assessing the condition of a patient, estimating the risk of an investment or relevance of the search result. However, the properties of interest are often latent and hard to assess directly, making it difficult to obtain classification or regression labels, which are needed to learn a predictive models from observable features. In such cases, it is typically much easier to obtain relative comparison of two instances, i.e. to assess which one is more intense (with respect to the property of interest). One framework able to learn from such kind of supervised information is ranking SVM, and it will make a basis of our approach. Applications in bio-medical datasets typically have specific additional challenges. First, and the major one, is the limited amount of data examples, due to an expensive measuring technology, and/or infrequency of conditions of interest. Such limited number of examples makes both identification of patterns/models and their validation less useful and reliable. Repeated samples from the same subject are collected on multiple occasions over time, which breaks IID sample assumption and introduces dependency structure that needs to be taken into account more appropriately. Also, feature vectors are highdimensional, and typically of much higher cardinality than the number of samples, making models less useful and their learning less efficient. Hypothesis of this dissertation is that use of the functional norm regularization can help alleviating mentioned challenges, by improving generalization abilities and/or learning efficiency of predictive models, in this case specifically of the approaches based on the ranking SVM framework. The temporal nature of data was addressed with loss that fosters temporal smoothness of functional mapping, thus accounting for assumption that temporally proximate samples are more correlated. Large number of feature variables was handled using the sparsity inducing L1 norm, such that most of the features have zero effect in learned functional mapping. Proposed sparse (temporal) ranking objective is convex but non-differentiable, therefore smooth dual form is derived, taking the form of quadratic function with box constraints, which allows efficient optimization. For the case where there are multiple similar tasks, joint learning approach based on matrix norm regularization, using trace norm L* and sparse row L21 norm was also proposed. Alternate minimization with proximal optimization algorithm was developed to solve the mentioned multi-task objective. Generalization potentials of the proposed high-dimensional and multi-task ranking formulations were assessed in series of evaluations on synthetically generated and real datasets. The high-dimensional approach was applied to disease severity score learning from gene expression data in human influenza cases, and compared against several alternative approaches. Application resulted in scoring function with improved predictive performance, as measured by fraction of correctly ordered testing pairs, and a set of selected features of high robustness, according to three similarity measures. The multi-task approach was applied to three human viral infection problems, and for learning the exam scores in Math and English. Proposed formulation with mixed matrix norm was overall more accurate than formulations with single norm regularization.
Temple University--Theses
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BUSCEMI, Simona. "Ensemble methods for ranking data with and without position weights." Doctoral thesis, Università degli Studi di Palermo, 2020. http://hdl.handle.net/10447/395373.
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The main goal of this Thesis is to build suitable Ensemble Methods for ranking data with weights assigned to the items’positions, in the cases of rankings with and without ties.
The Thesis begins with the definition of a new rank correlation coefficient, able to take into account the importance of items’position. Inspired by the rank correlation coefficient, τ x , proposed by Emond and Mason (2002) for unweighted rankings and the weighted Kemeny distance proposed by García-Lapresta and Pérez-Román (2010), this work proposes τ x w , a new rank correlation coefficient corresponding to the weighted Kemeny distance. The new coefficient is analized analitically and empirically and represents the main core of the consensus ranking process. Simulations and applications to real cases are presented. In a second step, in order to detect which predictors better explain a phenomenon, the Thesis proposes decision trees for ranking data with and without weights, discussing and comparing the results. A simulation study is built up, showing the impact of different structures of weights on the ability of decision trees to describe data. In the third part, ensemble methods for ranking data, more specifically Bagging and Boosting, are introduced. Last but not least, a review on a different topic is inserted in this Thesis. The review compares a significant number of linear mixed model selection procedures available in the literature. The review represents the answer to a pressing issue in the framework of LMMs: how to identify the best approach to adopt in a specific case. The work outlines mainly all approaches found in literature. This review represents my first academic training in making research.The main goal of this Thesis is to build suitable Ensemble Methods for ranking data with weights assigned to the items’positions, in the cases of rankings with and without ties. The Thesis begins with the definition of a new rank correlation coefficient, able to take into account the importance of items’position. Inspired by the rank correlation coefficient, τ x , proposed by Emond and Mason (2002) for unweighted rankings and the weighted Kemeny distance proposed by García-Lapresta and Pérez-Román (2010), this work proposes τ x w , a new rank correlation coefficient corresponding to the weighted Kemeny distance. The new coefficient is analized analitically and empirically and represents the main core of the consensus ranking process. Simulations and applications to real cases are presented. In a second step, in order to detect which predictors better explain a phenomenon, the Thesis proposes decision trees for ranking data with and without weights, discussing and comparing the results. A simulation study is built up, showing the impact of different structures of weights on the ability of decision trees to describe data. In the third part, ensemble methods for ranking data, more specifically Bagging and Boosting, are introduced. Last but not least, a review on a different topic is inserted in this Thesis. The review compares a significant number of linear mixed model selection procedures available in the literature. The review represents the answer to a pressing issue in the framework of LMMs: how to identify the best approach to adopt in a specific case. The work outlines mainly all approaches found in literature. This review represents my first academic training in making research.
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Eryilmaz, Utkan. "Hybrid Ranking Approaches Based On Data Envelopment Analysis And Outranking Relations." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607999/index.pdf.
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In this study two different hybrid ranking approaches based on data envelopment analysis and outranking relations for ranking alternatives are proposed. Outranking relations are widely used in Multicriteria Decision Making (MCDM) for ranking the alternatives and appropriate in situations when we have limited information on the preference structure of the decision maker. Yet to apply these methods DM should provide exact values for method parameters (weights, thresholds etc.) as well as basic information such as alternative scores. DEA is used for classification of decision making units according to their efficiency scores in a non-parameteric way. The proposed hybrid approaches utilize
PROMETHEE (a well known method based on outranking relations) to construct outranking relations by pairwise comparisons and a technique similar to DEA crossefficiency ranking for aggregating comparisons. While first of the proposed approaches can deal with imprecise specification of criterion weights, second approach can utilize imprecise weights and thresholds.
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Lee, Chun-fan, and 李俊帆. "Fitting factor models for ranking data using efficient EM-type algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31227557.
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Wooton, Sharyl Stasser. "DATA ENVELOPMENT ANALYSIS: A TOOL FOR SECONDARY EDUCATION RANKING AND BENCHMARKING." Oxford, Ohio : Miami University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1050604854.
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Thesis (M.S.A.)--Miami University, Dept. of Computer Science and Systems Analysis, 2003.Title from first page of PDF document. Document formatted into pages; contains iii, 36 p. Includes bibliographical references (p. 35-36).
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Jiang, Chunyu. "DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.
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Sutanto, Taufik Edy. "Scalable fine-grained document clustering via ranking." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/107459/1/Taufik%20Edy_Sutanto_Thesis.pdf.
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Dealing with big data, this thesis presents a novel and effective approach of scalable document clustering via ranking. The proposed clustering methods address the high-dimensionality problem in clustering analysis by introducing effective and computationally efficient cluster representations. The clustering via ranking approach is applicable to semi-supervised and unsupervised clustering problems. The proposed methods are applicable to static data as well as streaming data. The methods have been successfully tested with big social media data providing interesting insight.
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Murugesan, Keerthiram. "CLUSTER-BASED TERM WEIGHTING AND DOCUMENT RANKING MODELS." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_theses/651.
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A term weighting scheme measures the importance of a term in a collection. A document ranking model uses these term weights to find the rank or score of a document in a collection. We present a series of cluster-based term weighting and document ranking models based on the TF-IDF and Okapi BM25 models. These term weighting and document ranking models update the inter-cluster and intra-cluster frequency components based on the generated clusters. These inter-cluster and intra-cluster frequency components are used for weighting the importance of a term in addition to the term and document frequency components. In this thesis, we will show how these models outperform the TF-IDF and Okapi BM25 models in document clustering and ranking.
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Trouet, Valerie, Jan Esper, Paul J. Krusic, Fredrik C. Ljungqvist, Juerg Luterbacher, Marco Carrer, Ed Cook, et al. "Ranking of tree-ring based temperature reconstructions of the past millennium." PERGAMON-ELSEVIER SCIENCE LTD, 2016. http://hdl.handle.net/10150/621352.
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Tree-ring chronologies are widely used to reconstruct high-to low-frequency variations in growing season temperatures over centuries to millennia. The relevance of these timeseries in large-scale climate reconstructions is often determined by the strength of their correlation against instrumental temperature data. However, this single criterion ignores several important quantitative and qualitative characteristics of tree-ring chronologies. Those characteristics are (i) data homogeneity, (ii) sample replication, (iii) growth coherence, (iv) chronology development, and (v) climate signal including the correlation with instrumental data. Based on these 5 characteristics, a reconstruction-scoring scheme is proposed and applied to 39 published, millennial-length temperature reconstructions from Asia, Europe, North America, and the Southern Hemisphere. Results reveal no reconstruction scores highest in every category and each has their own strengths and weaknesses. Reconstructions that perform better overall include N-Scan and Finland from Europe, E-Canada from North America, Yamal and Dzhelo from Asia. Reconstructions performing less well include W-Himalaya and Karakorum from Asia, Tatra and S-Finland from Europe, and Great Basin from North America. By providing a comprehensive set of criteria to evaluate tree-ring chronologies we hope to improve the development of large-scale temperature reconstructions spanning the past millennium. All reconstructions and their corresponding scores are provided at www.blogs.uni-mainz.de/fb09climatology. (C) 2016 Elsevier Ltd. All rights reserved.
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Alsaleh, Slah. "Recommending people in social networks using data mining." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61736/1/Slah_Alsaleh_Thesis.pdf.
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This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.
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Alexandridis, Roxana Antoanela. "Minimum disparity inference for discrete ranked set sampling data." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126033164.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also includes graphics. Includes bibliographical references (p. 121-124). Available online via OhioLINK's ETD Center
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Tuncer, Ceren. "A Dea-based Approach To Ranking Multi-criteria Alternatives." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607476/index.pdf.
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ABSTRACT
A DEA-BASED APPROACH TO RANKING
MULTI-CRITERIA ALTERNATIVES
Tuncer, Ceren
M.Sc., Department of Industrial Engineering
Supervisor: Prof. Dr. Murat Köksalan August 2006, 88 pages This thesis addresses the problem of ranking multi-criteria alternatives. A Data Envelopment Analysis (DEA)-based approach, the Method of the Area of the Efficiency Score Graph (AES) is proposed. Rather than assessing the alternatives with respect to the fixed original alternative set as done in the existing DEA-based ranking methods, AES considers the change in the efficiency scores of the alternatives while reducing the size of the alternative set. Producing a final score for each alternative that accounts for the progress of its efficiency score, AES favors alternatives that manage to improve quickly and maintain high levels of efficiency. The preferences of the Decision Maker (DM) are incorporated into the analysis in the form of weight restrictions. The utilization of the AES scores of the alternatives in an incremental clustering algorithm is also proposed. The AES Method is applied to rank MBA programs worldwide, sorting of the programs is also performed using their AES scores. Results are compared to another DEA-based ranking method. Keywords: Ranking, data envelopment analysis, weight restrictions.
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Smart, Edward. "Detecting abnormalities in aircraft flight data and ranking their impact on the flight." Thesis, University of Portsmouth, 2011. https://researchportal.port.ac.uk/portal/en/theses/detecting-abnormalities-in-aircraft-flight-data-and-ranking-their-impact-on-the-flight(d9678b70-41e6-459a-82fb-ba2d12a0f971).html.
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To the best of the author’s knowledge, this is one of the first times that a large quantity of flight data has been studied in order to improve safety. A two phase novelty detection approach to locating abnormalities in the descent phase of aircraft flight data is presented. It has the ability to model normal time series data by analysing snapshots at chosen heights in the descent, weight individual abnormalities and quantitatively assess the overall level of abnormality of a flight during the descent. The approach expands on a recommendation by the UK Air Accident Investigation Branch to the UK Civil Aviation Authority. The first phase identifies and quantifies abnormalities at certain heights in a flight. The second phase ranks all flights to identify the most abnormal; each phase using a one class classifier. For both the first and second phases, the Support Vector Machine (SVM), the Mixture of Gaussians and the K-means one class classifiers are compared. The method is tested using a dataset containing manually labelled abnormal flights. The results show that the SVM provides the best detection rates and that the approach identifies unseen abnormalities with a high rate of accuracy. Furthermore, the method outperforms the event based approach currently in use. The feature selection tool F-score is used to identify differences between the abnormal and normal datasets. It identifies the heights where the discrimination between the two sets is largest and the aircraft parameters most responsible for these variations.
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Varadarajan, Ramakrishna R. "Ranked Search on Data Graphs." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/220.
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Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity – users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top-k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.
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Paulsson, Anton. "Using clickthrough data to optimize search result ranking : An evaluation of clickthrough data in terms of relevancy and efficiency." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14098.
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Search engines are in a constant need for improvements as the rapid growth of information is affecting the search engines ability to return documents with high relevance. Search results are being lost in between pages and the search algorithms are being exploited to gain a higher ranking on the documents. This study attempts to minimize those two issues, as well as increasing the relevancy of search results by usage of clickthrough data to add another layer of weighting the search results. Results from the evaluation indicate that clickthrough data in fact can be used to gain more relevant search results.
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Romanenkov, Yuri, Viktor Kosenko, Olena Lobach, Evgen Grinchenko, and Marina Grinchenko. "The method for ranking quasi-optimal alternatives in interval game models against nature." Thesis, National Technical University "Kharkiv Polytechnic Institute", 2019. http://repository.kpi.kharkov.ua/handle/KhPI-Press/47215.
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The task of selecting the optimal strategy in the interval game with nature is considered; in particular, the situation when in the interactive dialogue of an analyst and decision support system there are cases of objective ambiguity caused, on the one hand, by interval uncertainty of data, and on the other hand – by the chosen model of the task formalization. The method for ranking quasioptimal alternatives in interval game models against nature is proposed, which enables comparing interval alternatives in cases of classical interval ambiguity. In this case, the function of the analyst preferences is used with respect to the values of the criterion that help determine the indicators for the quantitative ranking of alternatives. By selecting a specific type of the preference function, the researcher artificially converts the primary uncertainty of the data into the uncertainty of the preference function form, which nevertheless enables avoiding the ambiguity in the "fuzzy" areas of quasi-optimal alternatives.
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Leung, Hiu-lan, and 梁曉蘭. "Wandering ideal point models for single or multi-attribute ranking data: a Bayesian approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29552357.
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Ibstedt, Julia, Elsa Rådahl, Erik Turesson, and Voorde Magdalena vande. "Application and Further Development of TrueSkill™ Ranking in Sports." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384863.
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The aim of this study was to explore the ranking model TrueSkill™ developed by Microsoft, applying it on various sports and constructing extensions to the model. Two different inference methods for TrueSkill was constructed using Gibbs sampling and message passing. Additionally, the sequential method using Gibbs sampling was successfully extended into a batch method, in order to eliminate game order dependency and creating a fairer, although computationally heavier, ranking system. All methods were further implemented with extensions for taking home team advantage, score difference and finally a combination of the two into consideration. The methods were applied on football (Premier League), ice hockey (NHL), and tennis (ATP Tour) and evaluated on the accuracy of their predictions before each game. On football, the extensions improved the prediction accuracy from 55.79% to 58.95% for the sequential methods, while the vanilla Gibbs batch method reached the accuracy of 57.37%. Altogether, the extensions improved the performance of the vanilla methods when applied on all data sets. The home team advantage performed better than the score difference on both football and ice hockey, while the combination of the two reached the highest accuracy. The Gibbs batch method had the highest prediction accuracy on the vanilla model for all sports. The results of this study imply that TrueSkill could be considered a useful ranking model for other sports as well, especially if tuned and implemented with extensions suitable for the particular sport.
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Alsarem, Mazen [Verfasser], and Harald [Akademischer Betreuer] Kosch. "Semantic Snippets via Query-Biased Ranking of Linked Data Entities / Mazen Alsarem ; Betreuer: Harald Kosch." Passau : Universität Passau, 2017. http://d-nb.info/1124229639/34.
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Triperina, Evangelia. "Visual interactive knowledge management for multicriteria decision making and ranking in linked open data environments." Thesis, Limoges, 2020. http://www.theses.fr/2020LIMO0010.
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Le doctorat impliqués la recherche dans le domaine des représentations visuelles assistées par des technologies sémantiques et des ontologies afin de soutenir les décisions et les procédures d'élaboration des politiques, dans le cadre de la recherche et des systèmes d'information académique. Les visualisations seront également prises en charge par l'exploration de données et les processus d'extraction de connaissances dans l'environnement de données liées. Pour élaborer, les techniques d'analyse visuelle seront utilisées pour l'organisation des visualisations afin de présenter l'information de manière à utiliser les capacités perceptuelles humaines et aideront éventuellement les procédures de prise de décision et de prise de décision. En outre, la représentation visuelle et, par conséquent, les processus décisionnels et décisionnels seront améliorés au moyen des technologies sémantiques basées sur des modèles conceptuels sous forme d'ontologies. Ainsi, l'objectif principal de la thèse de doctorat proposée consiste en la combinaison des technologies sémantiques clés et des techniques de visualisation interactive basées principalement sur la perception du graphique afin de rendre les systèmes de prise de décision plus efficaces. Le domaine de la demande sera le système de recherche et d'information académiqueThe dissertation herein involves research in the field of the visual representations aided by semantic technologies and ontologies in order to support decisions and policy making procedures, in the framework of research and academic information systems. The visualizations will be also supported by data mining and knowledge extraction processes in the linked data environment. To elaborate, visual analytics’ techniques will be employed for the organization of the visualizations in order to present the information in such a way that will utilize the human perceptual abilities and that will eventually assist the decision support and policy making procedures. Furthermore, the visual representation and consequently the decision and policy making processes will be ameliorated by the means of the semantic technologies based on conceptual models in the form of ontologies. Thus, the main objective of the proposed doctoral thesis consists the combination of the key semantic technologies with interactive visualisations techniques based mainly on graph’s perception in order to make decision support systems more effective. The application field will be the research and academic information systems
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Lee, Myunghyun. "Measuring and Ranking Efficiency of Major Airports in the United States Using Data Envelopment Analysis." Master's thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/46532.
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An airport is an important piece of infrastructure in air transportation system. This project focuses on measuring and ranking the efficiency of airports in the United States using the basic DEA, Ranking DEA, Goal programming and DEA and TOPSIS.
In general, airport authorities of relatively inefficient airports are trying to benchmark the operational strategies of efficient airports. This project focuses on evaluating hub airports in the United States.
ATL, LAX, and MEM airports are relatively efficient among forty four hub airports in the United States based on the performances and airport facilities of the 2000 year when the results of all applied methods in this project, the basic DEA ranking, the Cross Efficiency ranking, the Andersen-Petersen ranking and TOPSIS ranking method, are compared. The implication of this project is that airport authorities in the United States would benchmark these three airports to maximize operation and management efficiency for their airports. In general, most of the airports are handling passengers and freight. Therefore, ATL and LAX would be the most efficient hub airports in the United States. The capacities of airport facilities and more appropriate input data like financial data should be considered in the follow up research.Master of Science
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Theobald, Martin. "TopX efficient and versatile top-k query processing for text, structured, and semistructured data." Saarbrücken VDM Verlag Dr. Müller, 2006. http://d-nb.info/99139089X/04.
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Mukherjee, Somshree. "Ranking System for IoT Industry Platform." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204571.
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The Internet of Things (IoT) has seen a huge growth spurt in the last few years which has resulted in the need for more standardised IoT technology. Because of this, numerous IoT platforms have sprung up that offer a variety of features and use different technologies which may not necessarily be compliant with each other or with other technologies. Companies that wish to enter theIoT market are in constant need to find the most suitable IoT platform for their business and have a certain set of requirements that need to be fulfilled by the IoT platforms in order for the application to be fully functional. The problem that this thesis project is trying to address is a standardised procedure for selecting the IoT platforms. The project aims to suggest a list of requirements derived from the available IoT architecture models, that must be followed by IoT applications in general, and a subset of these requirements may be specified by the companies as essentials for their application. This thesis project also aims at development of a Web platform to automate this process, by listing the requirements on this website and allowing companies to input their choices,and accordingly show them the list of IoT platforms that comply with their input requirements. A simple Weighted Sum Model is used to rank the search result in order to prioritise the IoT platforms in order of the features that they provide. This thesis project also infers the best IoT architectural model available based on a comparative study of three major IoT architectures with respect to the requirements proposed. Hence the project concludes that this Web platform will ease the process of searching for the right IoT platform andthe companies can therefore make an informed decision about the kind of IoT platform that they should use, thereby reducing their time spent on market research and hence their time-to-market.
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Chen, Haiying. "Ranked set sampling for binary and ordered categorical variables with applications in health survey data." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1092770729.
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Thesis (Ph. D.)--Ohio State University, 2004.Title from first page of PDF file. Document formatted into pages; contains xiii, 109 p.; also includes graphics Includes bibliographical references (p. 99-102). Available online via OhioLINK's ETD Center
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Chuck, Lisa. "A PREDICTIVE MODEL FOR BENCHMARKING ACADEMIC PROGRAMS (PBAP)USING U.S. NEWS RANKING DATA FOR ENGINEERING COLLEGES OFFERING GRADU." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2397.
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Improving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study was to develop a predictive model for benchmarking academic programs (pBAP) for engineering colleges. The 2005 U.S. News ranking data for graduate engineering programs were used to create a four-tier predictive model (pBAP). The pBAP model correctly classified 81.9% of the cases in their respective tier. To test the predictive accuracy of the pBAP model, the 2005 U.S .News data were entered into the pBAP variate developed using the 2004 U.S. News data. The model predicted that 88.9% of the institutions would remain in the same ranking tier in the 2005 U.S. News rankings (compared with 87.7% in the actual data), and 11.1% of the institutions would demonstrate tier movement (compared with an actual 12.3% movement in the actual data). The likelihood of improving an institution's standing in the rankings was greater when increasing the values of 3 of the 11 variables in the U.S. News model: peer assessment score, recruiter assessment score, and research expenditures.Ed.D.
Department of Educational Research, Technology and Leadership
Education
Educational Leadership
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Marby, Josephine, and Ying Chen. "Ranking risks and opportunities of African cities : - A data-driven model to support MNE’s FDI strategies." Thesis, KTH, Industriell Management, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210315.
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The purpose of this paper is to build a model that incorporates current data of Africa, both regarding risks and opportunities into a strategic framework that should enable a more informed foreign direct investment decision for multinational enterprises (MNEs). The parameters used in the model were carefully chosen as determinants to foreign direct investment (FDI) based on extensive literature reviews. The model currently covers 101 major cities in 40 African countries. The model calculates and ranks indexes of African cities in terms of prospective investment opportunities. It is a general model with the flexibility of adapting to the user’s specific needs, since they can be highly heterogeneous depending on the industry and the type of to FDI considers. To test the validity of the model, standardized weights were used and the results were compared to current reports of FDI inflows to Africa. The results given by the model were to some extent compliable with the result of current FDI inflows, which thereby can be seen as sign of validity.
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MARBY, JOSEPHINE, and YING CHEN. "Ranking risks and opportunities of African cities : A data-driven model to support MNE’s FDI strategies." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223918.
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Chuck, Lisa Gay Marie. "A Predictive Model for Benchmarking Academic Programs (pBAP) Using U.S. News Ranking Data for Engineering Colleges Offering Graduate Programs." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2396.
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Improving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study was to develop a predictive model for benchmarking academic programs (pBAP) for engineering colleges. The 2005 U.S. News ranking data for graduate engineering programs were used to create a four-tier predictive model (pBAP). The pBAP model correctly classified 81.9% of the cases in their respective tier. To test the predictive accuracy of the pBAP model, the 2005 U.S .News data were entered into the pBAP variate developed using the 2004 U.S. News data. The model predicted that 88.9% of the institutions would remain in the same ranking tier in the 2005 U.S. News rankings (compared with 87.7% in the actual data), and 11.1% of the institutions would demonstrate tier movement (compared with an actual 12.3% movement in the actual data). The likelihood of improving an institution's standing in the rankings was greater when increasing the values of 3 of the 11 variables in the U.S. News model: peer assessment score, recruiter assessment score, and research expenditures.Ed.D.
Department of Educational Research, Technology and Leadership
Education
Educational Leadership
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Snedden, Larry D. "Improving Search Ranking Using a Composite Scoring Approach." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/776.
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In this thesis, the improvement to relevance in computerized search results is studied. Information search tools return ranked lists of documents ordered by the relevance of the documents to the user supplied search. Using a small number of words and phrases to represent complex ideas and concepts causes user search queries to be information sparse. This sparsity challenges search tools to locate relevant documents for users. A review of the challenges to information searches helps to identify the problems and offer suggestions in improving current information search tools. Using the suggestions put forth by the Strategic Workshop on Information Retrieval in Lorne (SWIRL), a composite scoring approach (Composite Scorer) is developed. The Composite Scorer considers various aspects of information needs to improve the ranked results of search by returning records relevant to the user’s information need.
The Florida Fusion Center (FFC), a local law enforcement agency has a need for a more effective information search tool. Daily, the agency processes large amounts of police reports typically written as text documents. Current information search methods require inordinate amounts of time and skill to identify relevant police reports from their large collection of police reports.
An experiment conducted by FFC investigators contrasted the composite scoring approach against a common search scoring approach (TF/IDF). In the experiment, police investigators used a custom-built software interface to conduct several use case scenarios for searching for related documents to various criminal investigations. Those expert users then evaluated the results of the top ten ranked documents returned from both search scorers to measure the relevance to the user of the returned documents. The evaluations were collected and measurements used to evaluate the performance of the two scorers. A search with many irrelevant documents has a cost to the users in both time and potentially in unsolved crimes. A cost function contrasted the difference in cost between the two scoring methods for the use cases. Mean Average Precision (MAP) is a common method used to evaluate the performance of ranked list search results. MAP was computed for both scoring methods to provide a numeric value representing the accuracy of each scorer at returning relevant documents in the top-ten documents of a ranked list of search results.
The purpose of this study is to determine if a composite scoring approach to ranked lists, that considers multiple aspects of a user’s search, can improve the quality of search, returning greater numbers of relevant documents during an information search. This research contributes to the understanding of composite scoring methods to improve search results. Understanding the value of composite scoring methods allows researchers to evaluate, explore and possibly extend the approach, incorporating other information aspects such as word and document meaning.