Дисертації з теми "Causal machine learning"
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Moffett, Jeffrey P. "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/320.
Повний текст джерелаBethard, Steven John. "Finding event, temporal and causal structure in text: A machine learning approach." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284435.
Повний текст джерелаBalsa, Fernández Juan José. "Using causal tree algorithms with difference in difference methodology : a way to have causal inference in machine learning." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168527.
Повний текст джерелаbeen for a long time one of the main focus of the economist around the world. At the same time, the development of different statistical methodologies have deeply helps them to complement the economic theory with the different types of data. One of the newest developments in this area is the Machine Learning algorithms for Causal inference, which gives them the possibility of using huge amounts of data, combined with computational tools for much more precise results. Nevertheless, these algorithms have not implemented one of the most used methodologies in the public evaluation, the Difference in Difference methodology. This document proposes an estimator that combines the Honest Causal Tree of Athey and Imbens (2016) with the Difference in Difference framework, giving us the opportunity to obtain heterogeneous treatment effect. Although the proposed estimator has higher levels of Bias, MSE, and Variance in comparison with the OLS, it is able to find significant results in cases where OLS do not, and instead of estimate an Average Treatment Effect, it is able to estimate a treatment effect for each individual.
Goh, Siong Thye. "Machine learning approaches to challenging problems : interpretable imbalanced classification, interpretable density estimation, and causal inference." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119281.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 111-118).
In this thesis, I address three challenging machine-learning problems. The first problem that we address is the imbalanced data problem. We propose two algorithms to handle highly imbalanced classification problems. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. The second method uses an approximation in order to assist with scalability. Specifically, it follows a characterize-then-discriminate approach. The positive class is first characterized by boxes, and then each box boundary becomes a separate discriminative classifier. This method is computationally advantageous because it can be easily parallelized, and considers only the relevant regions of the feature space. The second problem is a density estimation problem for categorical data sets. We present tree- and list- structured density estimation methods for binary/categorical data. We present three generative models, where the first one allows the user to specify the number of desired leaves in the tree within a Bayesian prior. The second model allows the user to specify the desired number of branches within the prior. The third model returns lists (rather than trees) and allows the user to specify the desired number of rules and the length of rules within the prior. Finally, we present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. Strictly, both treatment and control outcomes must be measured for each unit in order to perform supervised learning. However, in practice, only one outcome can be observed per unit. To overcome the problem that both treatment and control outcomes for the same unit are required for supervised learning, we propose surrogate loss functions that incorporate both treatment and control data. The new surrogates yield tighter bounds than the sum of the losses for the treatment and control groups. A specific choice of loss function, namely a type of hinge loss, yields a minimax support vector machine formulation. The resulting optimization problem requires the solution to only a single convex optimization problem, incorporating both treatment and control units, and it enables the kernel trick to be used to handle nonlinear (also non-parametric) estimation.
by Siong Thye Goh.
Ph. D.
Hagerty, Nicholas L. "Bayesian Network Modeling of Causal Relationships in Polymer Models." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619009432971036.
Повний текст джерелаLash, Michael Timothy. "Optimizing outcomes via inverse classification." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6602.
Повний текст джерелаKaiser, Michael Rainer Johann [Verfasser], and Florian [Akademischer Betreuer] Englmaier. "From causal inference to machine learning : four essays in empirical economics / Michael Rainer Johann Kaiser ; Betreuer: Florian Englmaier." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021. http://d-nb.info/1229835709/34.
Повний текст джерелаMiranda, Ackerman Eduardo Jacobo. "Extracting Causal Relations between News Topics from Distributed Sources." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130066.
Повний текст джерелаHazan, Amaury. "Musical expectation modelling from audio : a causal mid-level approach to predictive representation and learning of spectro-temporal events." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/22721.
Повний текст джерелаEsta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante de como procesamos la música que oímos. Muchos fenómenos relacionados con el procesamiento de la música están vinculados a una capacidad para anticipar la continuación de una pieza de música. Nos enfocaremos en un acercamiento estadístico de la expectativa musical, modelando los procesos de aprendizaje y de predicción de las regularidades espectro-temporales de forma causal. El principio de modelado estadístico de la expectativa se puede aplicar a varias representaciones de estructuras musicales, desde las notaciones simbólicas a la señales de audio. Primero demostramos que ciertos algoritmos de aprendizaje de secuencias se pueden usar y evaluar en el contexto de la percepción y el aprendizaje de secuencias auditivas. Luego, proponemos una representación, denominada qué/cuándo, para representar eventos musicales de una forma que permite describir y aprender la estructura secuencial de unidades acústicas en señales de audio musical. Aplicamos esta representación para describir y anticipar características tímbricas y ritmos. Sugerimos que se pueden explotar las propiedades del modelo de expectativa para resolver tareas de análisis como la segmentación estructural de piezas musicales. Finalmente, exploramos las implicaciones de nuestro modelo a la hora de definir nuevas aplicaciones en el contexto de la transcripción en tiempo real, la síntesis concatenativa y la visualización.
Ziebart, Brian D. "Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/17.
Повний текст джерелаKravchenko, Evgenija. "Association between cognitive measures, global brain surface area, genetics, and screen-time in young adolescents : Estimation of causal inference with machine learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290033.
Повний текст джерелаSkärmaktivitet som att titta på TV och video, spela videospel och använda sociala medier har blivit en populär fritidsaktivitet för barn och ungdomar. Effekten av skärmtid har varit ett mycket debatterat ämne; det finns dock fortfarande mycket lite kunskap om det. Med hjälp av datasetet från Adolescent Brain Cognitive Development långtidsstudien kunde 4 217 ungdomar, som uppfyllde specifika krav, väljas ut för detta avhandlingsprojekt efter bearbetning av datan. Detta avhandlingsprojekt undersökte kausal ordning mellan genetisk effekt (Polygenic scores (PGS) för kognitiv prestation), skärmtidsaktivitet, hjärnmorfologi (strukturell Magnet Resonans Imaging (sMRI) för hjärnans ytarea och hjärnbarks tjocklek), brist på ihärdighet och kognitiv förmåga (kristalliserad IQ) med en maskininlärningsalgoritm DirectLiNGAM. Tydlig korrelation mellan skärmaktivitet och PGS hittades för alla typer av skärmaktiviteter men endast videospel och sociala medier korrelerade till den globala ytarean. Dessutom verkar TV och video påverka brist på ihärdighet och brist på ihärdighet i sin tur påverkar hur mycket tid som spenderas på videospel. Dessa resultat antyder att olika typer av sociala medier inte är så lika som vi trodde och kan påverka ungdomar olika. Sammanlagt stöder dessa upptäckter tidigare forskning om skärmtidseffekt på brist på ihärdighet, hjärnmorfologi och kognitiv förmåga och föreslår en ny kausal inferens mellan genetik och skärmtid. Slutligen ledde algoritmen som användes i detta avhandlingsprojekt fram till rimliga kausala ordningar och kan ses som ett mycket bra komplement till dagens kausala modellering.
Olsen, Catharina. "Causal inference and prior integration in bioinformatics using information theory." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209401.
Повний текст джерелаAnother important problem in bioinformatics is the question of how the inferred networks’ quality can be evaluated. The current best practice is a two step procedure. In the first step, the highest scoring interactions are compared to known interactions stored in biological databases. The inferred networks passes this quality assessment if there is a large overlap with the known interactions. In this case, a second step is carried out in which unknown but high scoring and thus promising new interactions are validated ’by hand’ via laboratory experiments. Unfortunately when integrating prior knowledge in the inference procedure, this validation procedure would be biased by using the same information in both the inference and the validation. Therefore, it would no longer allow an independent validation of the resulting network.
The main contribution of this thesis is a complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks. Its components are i) a method that integrates genomic data and prior knowledge to infer directed networks, ii) its implementation in an R/Bioconductor package and iii) a web application to retrieve prior knowledge from PubMed abstracts and biological databases. To infer directed networks from genomic data and prior knowledge, we propose a two step procedure: First, we adapt the pairwise feature selection strategy mRMR to integrate prior knowledge in order to obtain the network’s skeleton. Then for the subsequent orientation phase of the algorithm, we extend a criterion based on interaction information to include prior knowledge. The implementation of this method is available both as part of the prior retrieval tool Predictive Networks and as a stand-alone R/Bioconductor package named predictionet.
Furthermore, we propose a fully data-driven quantitative validation of such directed networks using experimental knock-down data: We start by identifying the set of genes that was truly affected by the perturbation experiment. The rationale of our validation procedure is that these truly affected genes should also be part of the perturbed gene’s childhood in the inferred network. Consequently, we can compute a performance score
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.
Повний текст джерелаMeganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.
Повний текст джерелаFox-Roberts, Patrick Kirk. "An examination of the causes of bias in semi-supervised learning." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648460.
Повний текст джерелаBequé, Artem. "Verfahren des maschinellen Lernens zur Entscheidungsunterstützung." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19421.
Повний текст джерелаNowadays right decisions, being it strategic or operative, are important for every company, since these contribute directly to an overall success. This success can be measured based on quantitative metrics, for example, by the number of loyal customers or the number of incremental purchases. These decisions are typically made based on the historical data that relates to all functions of the company in general and to customers in particular. Thus, companies seek to analyze this data and apply obtained knowlegde in decision making. Classification problems represent an example of such decisions. Classification problems are best solved, when techniques of classical statistics and these of machine learning are applied, since both of them are able to analyze huge amount of data, to detect dependencies of the data patterns, and to produce probability, which represents the basis for the decision making. I apply these techniques and examine their suitability based on correlative models for decision making in credit scoring and further extend the work by causal predictive models for direct marketing. In detail, I analyze the suitability of techniques of machine learning for credit scoring alongside multiple dimensions, I examine the ability to produce calibrated probabilities and apply techniques to improve the probability estimations. I further develop and propose a synergy heuristic between the methods of classical statistics and techniques of machine learning to improve the prediction quality of the former, and finally apply conversion models to turn machine learning techqiques to account for causal relationship between marketing campaigns and customer behavior in direct marketing. The work has shown that the techniques of machine learning represent a suitable alternative to the methods of classical statistics for decision making and should be considered not only in research but also should find their practical application in real-world practices.
Valente, Marica. "Essays on Applied Microeconomics." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22184.
Повний текст джерелаIn economics, researchers use a wide variety of strategies for attempting to draw causal inference from observational data. New developments in the causal inference literature focus on the combination of predictive methods and causal questions. These methods allow researchers to answer new research questions as well as provide new opportunities to address older research question in the literature. This dissertation entails empirical work in the fields of (i) environmental economics: I evaluate waste pricing policies using synthetic controls and machine learning methods; (ii) labor and migration economics: I identify and quantify unreported farm labor induced by a sudden migrant inflow; (iii) conflict economics: I evaluate the economic costs of an hybrid war, namely, the Donbass war in Ukraine. The contribution of this dissertation is threefold. First, I combine novel data sources and provide unique datasets. Second, I apply and tailor modern evaluation methods to the estimation of policy-relevant causal parameters in various fields of economics. Third, I compare recent versus traditional econometric approaches previously employed by the literature. My dissertation shows that modern econometric techniques hold great promise for improving the accuracy and credibility of causal inference and policy evaluation.
Chapala, Usha Kiran, and Sridhar Peteti. "Continuous Video Quality of Experience Modelling using Machine Learning Model Trees." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17814.
Повний текст джерелаGiuzio, Antonio. "Machine Learning per la predizione dell’outcome riabilitativo e per la scelta della componentistica protesica in pazienti con amputazione transfemorale: alberi decisionali e alberi causali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаDelacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.
Повний текст джерелаMiller, John William. "Differentiation between causes of optic disc swelling using retinal layer shape features." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6215.
Повний текст джерелаvon, Hacht Johan. "Anomaly Detection for Root Cause Analysis in System Logs using Long Short-Term Memory." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301656.
Повний текст джерелаMånga mjukvarusystem testas för att försäkra att de fungerar som de ska. Ibland kan ett test misslyckas och i detta fall är det viktigt att förstå varför det gick fel. Detta kan bli problematiskt när mjukvarusystemen växer och blir mer komplexa eftersom att denna uppgift kan bli icke trivial och ta mycket tid. Om man skulle kunna automatisera felsökningsprocessen skulle det kunna spara mycket tid för de invloverade utvecklarna. Denna rapport undersöker användningen av en Long Short-Term Memory (LSTM) anomalidetektor för grundorsaksanalys i loggar. Implementationen utvärderas genom en kvantitativ och kvalitativ undersökning. Den kvantitativa undersökningen utvärderar prestandan av anomalidetektorn med precision, recall och F1 mått. Artificiellt insatta anomalier används för att kunna beräkna dessa mått eftersom att det inte finns etiketter i den använda datan. Implementationen jämförs också med en annan simpel anomalidetektor. Den kvalitativa undersökning utvärderar hur användbar anomalidetektorn är för grundorsaksanalys för misslyckade tester. Detta utvärderades genom intervjuer med en expert inom mjukvaran som producerade datan som användes in denna rapport. Resultaten visar att LSTM anomalidetektorn lyckades nå ett högre F1 mått jämfört med den simpla modellen. Detta tack vare att den kunde upptäcka ovanliga loggmeddelanden och loggmeddelanden som skedde i fel ordning. De kvalitativa resultaten pekar på att anomalidetektorn kan användas för grundorsaksanalys för misslyckade tester. I många av de misslyckade tester som utvärderades kunde experten hitta anledningen till att felet misslyckades genom det som hittades av anomalidetektorn. Även om detektorn inte hittade den exakta orsaken till att testet misslyckades så kan den belysa en vissa del av mjukvaran. Detta betyder att just den delen av mjukvaran producerad många anomalier i loggarna. Med denna information kan experten kontakta andra personer som känner till den delen av mjukvaran bättre för hjälp. Anomalidetektorn automatiskt den information som är viktig för att experten ska kunna utföra grundorsaksanalys. Tack vare detta kan experten spendera mindre tid på denna uppgift. Med vissa förbättringar skulle det också kunna vara möjligt för mindre erfarna utvecklare att använda anomalidetektorn. Detta minskar behovet för en expert.
Lattimore, Finnian Rachel. "Learning how to act: making good decisions with machine learning." Phd thesis, 2017. http://hdl.handle.net/1885/144602.
Повний текст джерелаBergman, Ruth. "Learning World Models in Environments with Manifest Causal Structure." 1995. http://hdl.handle.net/1721.1/6777.
Повний текст джерелаAveritt, Amelia Jean. "Machine Learning Methods for Causal Inference with Observational Biomedical Data." Thesis, 2020. https://doi.org/10.7916/d8-je06-eh12.
Повний текст джерелаEr, Emrah. "Applications of machine learning to agricultural land values: prediction and causal inference." Diss., 2018. http://hdl.handle.net/2097/39313.
Повний текст джерелаDepartment of Agricultural Economics
Nathan P. Hendricks
This dissertation focuses on the prediction of agricultural land values and the effects of water rights on land values using machine learning algorithms and hedonic pricing methods. I predict agricultural land values with different machine learning algorithms, including ridge regression, least absolute shrinkage and selection operator, random forests, and extreme gradient boosting methods. To analyze the causal effects of water right seniority on agricultural land values, I use the double-selection LASSO technique. The second chapter presents the data used in the dissertation. A unique set of parcel sales from Property Valuation Division of Kansas constitute the backbone of the data used in the estimation. Along with parcel sales data, I collected detailed basis, water, tax, soil, weather, and urban influence data. This chapter provides detailed explanation of various data sources and variable construction processes. The third chapter presents different machine learning models for irrigated agricultural land price predictions in Kansas. Researchers, and policymakers use different models and data sets for price prediction. Recently developed machine learning methods have the power to improve the predictive ability of the models estimated. In this chapter I estimate several machine learning models for predicting the agricultural land values in Kansas. Results indicate that the predictive power of the machine learning methods are stronger compared to standard econometric methods. Median absolute error in extreme gradient boosting estimation is 0.1312 whereas it is 0.6528 in simple OLS model. The fourth chapter examines whether water right seniority is capitalized into irrigated agricultural land values in Kansas. Using a unique data set of irrigated agricultural land sales, I analyze the causal effect of water right seniority on agricultural land values. A possible concern during the estimation of hedonic models is the omitted variable bias so we use double-selection LASSO regression and its variable selection properties to overcome the omitted variable bias. I also estimate generalized additive models to analyze the nonlinearities that may exist. Results show that water rights have a positive impact on irrigated land prices in Kansas. An additional year of water right seniority causes irrigated land value to increase nearly $17 per acre. Further analysis also suggest a nonlinear relationship between seniority and agricultural land prices.
"Causal discovery from non-experimental data: 基於非實驗數據的因果分析". 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291270.
Повний текст джерелаThesis Ph.D. Chinese University of Hong Kong 2014.
Includes bibliographical references (leaves 140-146).
Abstracts also in Chinese.
Title from PDF title page (viewed on 14, September, 2016).
Chen, Zhitang.
Brouillard, Philippe. "Apprentissage de modèles causaux par réseaux de neurones artificiels." Thesis, 2020. http://hdl.handle.net/1866/25096.
Повний текст джерелаIn this thesis by articles, we study the learning of causal models from data. The goal of this entreprise is to gain a better understanding of data and to be able to predict the effect of a change on some variables of a given system. Since discovering causal relationships is fundamental in science, causal structure learning methods have applications in many fields that range from genomics, biology, and economy. We present two new methods that have the particularity of being non-linear methods learning causal models casted as a continuous optimization problem subject to a constraint. Previously, causal strutural methods addressed this search problem by using greedy search heuristics. Recently, a new continuous acyclity constraint has allowed to address the problem differently. In the first article, we present one of these non-linear method: GraN-DAG. Under some assumptions, GraN-DAG can learn a causal graph from observational data. Since the publi- cation of this first article, several alternatives methods have been proposed by the community by using the same continuous-constrained optimization formulation. However, none of these methods support interventional data. Nevertheless, interventions reduce the identifiability problem and allow the use of more expressive neural architectures. In the second article, we present another method, DCDI, that has the particularity to leverage data with several kinds of interventions. Since the identifiabiliy issue is less severe, one of the two instantia- tions of DCDI is a universal density approximator. For both methods, we show that these methods have really good performances on synthetic and real-world tasks comparatively to other classical methods.
Pinto, Ana Raquel de Melo. "Using Machine Learning to Measure Democracy and Economic Development, and the Causal Relationship Between the Two." Master's thesis, 2021. https://hdl.handle.net/10216/137617.
Повний текст джерелаLu, Rui. "Feature Selection for High Dimensional Causal Inference." Thesis, 2020. https://doi.org/10.7916/d8-52mk-ft68.
Повний текст джерелаJiang, Tammy. "Suicide and non-fatal suicide attempts among persons with depression in the population of Denmark." Thesis, 2021. https://hdl.handle.net/2144/42580.
Повний текст джерела(5929691), Asish Ghoshal. "Efficient Algorithms for Learning Combinatorial Structures from Limited Data." Thesis, 2019.
Знайти повний текст джерела"Detecting Frames and Causal Relationships in Climate Change Related Text Databases Based on Semantic Features." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49062.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Computer Science 2018
Bhattacharya, Indranil. "Feature Selection under Multicollinearity & Causal Inference on Time Series." Thesis, 2017. http://etd.iisc.ernet.in/2005/3980.
Повний текст джерелаChuang, Wen-Tze, and 莊文澤. "Detecting Critical Timing Paths Caused by Dynamic Voltage Drop Using Machine Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j9nhr6.
Повний текст джерела國立臺灣大學
資訊工程學研究所
107
Timing constrain will reduce operational frequency of large integrated circuits or system-on-a-chip, and it is often caused by setup timing violation which would be influenced by dynamic voltage drop, can be referred to as maximum timing pushout. This problem is exacerbated in the FinFET designs. This thesis proposes a method using machine learning techniques to predict critical scenarios quickly for analyzing dynamic voltage drop and critical timing paths predictor for accurate timing analysis. First, we use a classification model to predict critical level of timing paths, and use a regression model or a ranking model to predict ranking of the critical timing paths afterwards. Next, we can determine the critical scenarios for analyzing dynamic voltage drop. After the analysis, we use the classification model to predict critical level of timing paths which is similar to the first step. In our method, the best classification model can achieve about 90% accuracy, and 80% of hit-rate in Top-5 critical scenarios predicting.
Olowe, Feranmi Jeremiah. "Spatial prediction of flood susceptible areas using machine learning approach: a focus on west african region." Master's thesis, 2021. http://hdl.handle.net/10362/113893.
Повний текст джерелаThe constant change in the environment due to increasing urbanization and climate change has led to recurrent flood occurrences with a devastating impact on lives and properties. Therefore, it is essential to identify the factors that drive flood occurrences, and flood locations prone to flooding which can be achieved through the performance of Flood Susceptibility Modelling (FSM) utilizing stand-alone and hybrid machine learning models to attain accurate and sustainable results which can instigate mitigation measures and flood risk control. In this research, novel hybridizations of Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) was performed and equally as stand-alone models in Flood Susceptibility Modelling (FSM) and results of each model compared. First, feature selection and multi-collinearity analysis were performed to identify the predictive ability and the inter-relationship among the factors. Subsequently, IOE was performed as bivariate and multivariate statistical analysis to assess the correlation among the flood influencing factor’s classes with flooding and the overall influence (weight) of each factor on flooding. Subsequently, the weight generated was used in training the machine learning models. The performance of the proposed models was assessed using the popular Area Under Curve (AUC) and statistical metrics. Percentagewise, results attained reveals that DT-IOE hybrid model had the highest prediction accuracy of 87.1% while the DT had the lowest prediction performance of 77.0%. Among the other models, the result attained highlight that the proposed hybrid of machine learning and statistical models had a higher performance than the stand-alone models which reflect the detailed assessment performed by the hybrid models. The final susceptibility maps derived revealed that about 21% of the study area are highly prone to flooding and it is revealed that human-induced factors do have a huge influence on flooding in the region.
Ferreira, Guerra Steve. "Une procédure de sélection automatique de la discrétisation optimale de la ligne du temps pour des méthodes longitudinales d’inférence causale." Thèse, 2017. http://hdl.handle.net/1866/20549.
Повний текст джерелаJehangiri, Ali Imran. "Distributed Anomaly Detection and Prevention for Virtual Platforms." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605F-2.
Повний текст джерелаMinerve, Mampaka Maluambanzila. "Quadri-dimensional approach for data analytics in mobile networks." Diss., 2018. http://hdl.handle.net/10500/25882.
Повний текст джерелаElectrical and Mining Engineering
M. Tech. (Electrical Engineering)