Log in

Relevant bibliographies by topics / Classification tree models

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Classification tree models'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Classification tree models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Classification tree models"

1

Verbyla, David L. "Classification trees: a new discrimination tool." Canadian Journal of Forest Research 17, no. 9 (September 1, 1987): 1150–52. http://dx.doi.org/10.1139/x87-177.

Full text

Abstract:

Classification trees are discriminant models structured as dichtomous keys. A simple classification tree is presented and contrasted with a linear discriminant function. Classification trees have several advantages when compared with linear discriminant analysis. The method is robust with respect to outlier cases. It is nonparametric and can use nominal, ordinal, interval, and ratio scaled predictor variables. Cross-validation is used during tree development to prevent overrating the tree with too many predictor variables. Missing values are handled by using surrogate splits based on nonmissing predictor variables. Classification trees, like linear discriminant analysis, have potential prediction bias and therefore should be validated before being accepted.

APA, Harvard, Vancouver, ISO, and other styles

2

Diligenti, M., P. Frasconi, and M. Gori. "Hidden tree markov models for document image classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 4 (April 2003): 520–24. http://dx.doi.org/10.1109/tpami.2003.1190578.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Povkhan, I. F. "THE METHOD OF BOUNDED CONSTRUCTIONS OF LOGICAL CLASSIFICATION TREES IN THE PROBLEM OF DISCRETE OBJECTS CLASSIFICATION." Ukrainian Journal of Information Technology 3, no. 1 (2021): 22–29. http://dx.doi.org/10.23939/ujit2021.03.022.

Full text

Abstract:

The problem of constructing a model of logical classification trees based on a limited method of selecting elementary features for geological data arrays is considered. A method for approximating an array of real data with a set of elementary features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree is proposed. This approach allows to ensure the necessary accuracy of the model, reduce its structural complexity, and achieve the necessary performance indicators. A limited method for constructing classification trees has been developed, which is aimed at completing only those paths (tiers) of the classification tree structure where there are the greatest number of errors (of all types) of classification. This approach to synthesizing the recognition model makes it possible to effectively regulate the complexity (accuracy) of the classification tree model that is being built, and it is advisable to use it in situations with restrictions on the hardware resources of the information system, restrictions on the accuracy and structural complexity of the model, restrictions on the structure, sequence and depth of recognition of the training sample data array. The limited scheme of synthesis of classification trees allows to build models almost 20 % faster. The constructed logical classification tree will accurately classify (recognize) the entire training sample that the model is based on, will have a minimal structure (structural complexity), and will consist of components – sets of elementary features as design vertices, tree attributes. Based on the proposed modification of the elementary feature selection method, software has been developed that allows working with a set of different types of applied problems. An approach to synthesizing new recognition models based on a limited logic tree scheme and selecting pre-pruning parameters is proposed. In other words, an effective scheme for recognizing discrete objects has been developed based on step-by-step evaluation and selection of sets of attributes (generalized features) based on selected paths in the classification tree structure at each stage of scheme synthesis.

APA, Harvard, Vancouver, ISO, and other styles

4

Maschler, Julia, Clement Atzberger, and Markus Immitzer. "Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data." Remote Sensing 10, no. 8 (August 3, 2018): 1218. http://dx.doi.org/10.3390/rs10081218.

Full text

Abstract:

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.

APA, Harvard, Vancouver, ISO, and other styles

5

Thoe, Wai, King Wah Choi, and Joseph Hun-wei Lee. "Predicting ‘very poor’ beach water quality gradings using classification tree." Journal of Water and Health 14, no. 1 (October 8, 2015): 97–108. http://dx.doi.org/10.2166/wh.2015.094.

Full text

Abstract:

A beach water quality prediction system has been developed in Hong Kong using multiple linear regression (MLR) models. However, linear models are found to be weak at capturing the infrequent ‘very poor’ water quality occasions when Escherichia coli (E. coli) concentration exceeds 610 counts/100 mL. This study uses a classification tree to increase the accuracy in predicting the ‘very poor’ water quality events at three Hong Kong beaches affected either by non-point source or point source pollution. Binary-output classification trees (to predict whether E. coli concentration exceeds 610 counts/100 mL) are developed over the periods before and after the implementation of the Harbour Area Treatment Scheme, when systematic changes in water quality were observed. Results show that classification trees can capture more ‘very poor’ events in both periods when compared to the corresponding linear models, with an increase in correct positives by an average of 20%. Classification trees are also developed at two beaches to predict the four-category Beach Water Quality Indices. They perform worse than the binary tree and give excessive false alarms of ‘very poor’ events. Finally, a combined modelling approach using both MLR model and classification tree is proposed to enhance the beach water quality prediction system for Hong Kong.

APA, Harvard, Vancouver, ISO, and other styles

6

Povkhan, Igor. "FEATURES OF SOFTWARE SOLUTIONS OF MODELS OF LOGICAL CLASSIFICATION TREES BASED ON SELECTION OF SETS OF ELEMENTARY FEATURES." Technical Sciences and Technologies, no. 4(22) (2020): 72–90. http://dx.doi.org/10.25140/2411-5363-2020-4(22)-72-90.

Full text

Abstract:

Urgency of the research.Currently there are several independent approaches (concepts) to solve the classification problem in the general setting, and the development of various concepts, approaches, methods, and models that cover the general issues of the theory of artificial intelligence and information systems, all of these approaches in a recognition theory have their advantages and disadvantages and form a single tool to solve applied problems of the theory of artificial intelligence. This study will focus on the current concept of decision trees (classification trees). The general problem of software (algorithmic) construction of logical recognition trees (classification) is considered. The object of this research is logical classification trees (LСT structures). The subject of the research is actual methods and algorithmic schemes for constructing logical classification trees. Target setting.The main existing methods and algorithms for working with arrays of discrete information in the construc-tion of recognition functions (classifiers) do not allow you to achieve a predetermined level of accuracy (efficiency) of the classification system and regulate their complexity in the construction process. However, this disadvantage is absent in meth-ods and schemes for building recognition systems based on the concept of logical classification trees (decision trees). That is, the coverage of the training sample the set of elementary signs in the case of LCT generates a fixed tree data structure (model LCT), which provides compression and conversion initial data TS, and therefore allows significant optimization and savings of hardware resources of the system, and is based on a single methodology – the optimal approximation test sample set of elementary features (attributes) that are included in some schema (operator) constructed in the learning process.Actual scientific researches and issues analysis. The possibility of an effective and economical software (algorithmic) scheme for constructing a logical classification tree (LCT structuremodel) based on the source arrays of training samples (arrays of discrete information) of a large sample.The research objective. Development of a simple and high-quality software method (algorithm and software system) for building models (structures) LCTfor large arrays of initial samples by synthesizing minimal forms of classification and recog-nition trees that provide an effective approximation of educational information with a set of ranked elementary features (at-tributes) is created on the basis of ascheme for branched feature selection in a wide range of applied problems.The statement of basic materials. We propose a general program scheme for constructing structures of logical classifi-cation trees, which for a given initial training sample builds a tree structure (classification model), which consists of a set of elementary features evaluated at each step of building the model for this sample. A method and ready-made software system build logic trees the main idea is to approximate the initial random sampling of the volume set of elementary features. This method provides the selection of the most informative (qualitative) elementary features from the source set when forming the current vertex of the logical tree (node). This approach allows to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent analysis.Conclusions. The developed and proposed mathematical support for constructing LCT structures (classification tree mod-els) allows it to be used for solving a wide range of practical problems of recognition and classification, and the prospectsfor further research may consist in creating a limited method of logical classification tree (LCT structures), which consists in maintaining the criterion for stopping the procedure for constructing a logical tree by the depth of the structure, optimizing its software implementations, as well as experimental studies of this method for a wider range of practicalproblems.

APA, Harvard, Vancouver, ISO, and other styles

7

Khoshgoftaar, Taghi M., and Naeem Seliya. "Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm." International Journal on Artificial Intelligence Tools 12, no. 03 (September 2003): 207–25. http://dx.doi.org/10.1142/s0218213003001204.

Full text

Abstract:

Predicting the quality of system modules prior to software testing and operations can benefit the software development team. Such a timely reliability estimation can be used to direct cost-effective quality improvement efforts to the high-risk modules. Tree-based software quality classification models based on software metrics are used to predict whether a software module is fault-prone or not fault-prone. They are white box quality estimation models with good accuracy, and are simple and easy to interpret. An in-depth study of calibrating classification trees for software quality estimation using the SPRINT decision tree algorithm is presented. Many classification algorithms have memory limitations including the requirement that datasets be memory resident. SPRINT removes all of these limitations and provides a fast and scalable analysis. It is an extension of a commonly used decision tree algorithm, CART, and provides a unique tree pruning technique based on the Minimum Description Length (MDL) principle. Combining the MDL pruning technique and the modified classification algorithm, SPRINT yields classification trees with useful accuracy. The case study used consists of software metrics collected from a very large telecommunications system. It is observed that classification trees built by SPRINT are more balanced and demonstrate better stability than those built by CART.

APA, Harvard, Vancouver, ISO, and other styles

8

Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.

Full text

Abstract:

Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a classification tree.in this paper, Classification Trees Method of Knowledge Discovery In Internet is given.

APA, Harvard, Vancouver, ISO, and other styles

9

Thakkar, Pooja. "Drug Classification using Black-box models and Interpretability." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1518–29. http://dx.doi.org/10.22214/ijraset.2021.38203.

Full text

Abstract:

Abstract: The focus of this study is on drug categorization utilising Machine Learning models, as well as interpretability utilizing LIME and SHAP to get a thorough understanding of the ML models. To do this, the researchers used machine learning models such as random forest, decision tree, and logistic regression to classify drugs. Then, using LIME and SHAP, they determined if these models were interpretable, which allowed them to better understand their results. It may be stated at the conclusion of this paper that LIME and SHAP can be utilised to get insight into a Machine Learning model and determine which attribute is accountable for the divergence in the outcomes. According to the LIME and SHAP results, it is also discovered that Random Forest and Decision Tree ML models are the best models to employ for drug classification, with Na to K and BP being the most significant characteristics for drug classification. Keywords: Machine Learning, Back-box models, LIME, SHAP, Decision Tree

APA, Harvard, Vancouver, ISO, and other styles

10

Lim, Chee Soon, Edy Tonnizam Mohamad, Mohammad Reza Motahari, Danial Jahed Armaghani, and Rosli Saad. "Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study." Applied Sciences 10, no. 17 (August 19, 2020): 5734. http://dx.doi.org/10.3390/app10175734.

Full text

Abstract:

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Classification tree models"

1

Liu, Dan. "Tree-based Models for Longitudinal Data." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1399972118.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Keller-Schmidt, Stephanie. "Stochastic Tree Models for Macroevolution." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-96504.

Full text

Abstract:

Phylogenetic trees capture the relationships between species and can be investigated by morphological and/or molecular data. When focusing on macroevolution, one considers the large-scale history of life with evolutionary changes affecting a single species of the entire clade leading to the enormous diversity of species obtained today. One major problem of biology is the explanation of this biodiversity. Therefore, one may ask which kind of macroevolutionary processes have given rise to observable tree shapes or patterns of species distribution which refers to the appearance of branching orders and time periods. Thus, with an increasing number of known species in the context of phylogenetic studies, testing hypotheses about evolution by analyzing the tree shape of the resulting phylogenetic trees became matter of particular interest. The attention of using those reconstructed phylogenies for studying evolutionary processes increased during the last decades. Many paleontologists (Raup et al., 1973; Gould et al., 1977; Gilinsky and Good, 1989; Nee, 2004) tried to describe such patterns of macroevolution by using models for growing trees. Those models describe stochastic processes to generate phylogenetic trees. Yule (1925) was the first who introduced such a model, the Equal Rate Markov (ERM) model, in the context of biological branching based on a continuous-time, uneven branching process. In the last decades, further dynamical models were proposed (Yule, 1925; Aldous, 1996; Nee, 2006; Rosen, 1978; Ford, 2005; Hernández-García et al., 2010) to address the investigation of tree shapes and hence, capture the rules of macroevolutionary forces. A common model, is the Aldous\\\' Branching (AB) model, which is known for generating trees with a similar structure of \\\"real\\\" trees. To infer those macroevolutionary forces structures, estimated trees are analyzed and compared to simulated trees generated by models. There are a few drawbacks on recent models such as a missing biological motivation or the generated tree shape does not fit well to one observed in empirical trees. The central aim of this thesis is the development and study of new biologically motivated approaches which might help to better understand or even discover biological forces which lead to the huge diversity of organisms. The first approach, called age model, can be defined as a stochastic procedure which describes the growth of binary trees by an iterative stochastic attachment of leaves, similar to the ERM model. At difference with the latter, the branching rate at each clade is no longer constant, but decreasing in time, i.e., with the age. Thus, species involved in recent speciation events have a tendency to speciate again. The second introduced model, is a branching process which mimics the evolution of species driven by innovations. The process involves a separation of time scales. Rare innovation events trigger rapid cascades of diversification where a feature combines with previously existing features. The model is called innovation model. Three data sets of estimated phylogenetic trees are used to analyze and compare the produced tree shape of the new growth models. A tree shape statistic considering a variety of imbalance measurements is performed. Results show that simulated trees of both growth models fit well to the tree shape observed in real trees. In a further study, a likelihood analysis is performed in order to rank models with respect to their ability to explain observed tree shapes. Results show that the likelihoods of the age model and the AB model are clearly correlated under the trees in the databases when considering small and medium-sized trees with up to 19 leaves. For a data set, representing of phylogenetic trees of protein families, the age model outperforms the AB model. But for another data set, representing phylogenetic trees of species, the AB model performs slightly better. To support this observation a further analysis using larger trees is necessary. But an exact computation of likelihoods for large trees implies a huge computational effort. Therefore, an efficient method for likelihood estimation is proposed and compared to the estimation using a naive sampling strategy. Nevertheless, both models describe the tree generation process in a way which is easy to interpret biologically. Another interesting field of research in biology is the coevolution between species. This is the interaction of species across groups such that the evolution of a species from one group can be triggered by a species from another group. Most prominent examples are systems of host species and their associated parasites. One problem is the reconciliation of the common history of both groups of species and to predict the associations between ancestral hosts and their parasites. To solve this problem some algorithmic methods have been developed in recent years. But only a few host parasite systems have been analyzed in sufficient detail which makes an evaluation of these methods complex. Within the scope of coevolution, the proposed age model is applied to the generation of cophylogenies to evaluate such host parasite reconciliation methods. The presented age model as well as the innovation model produce tree shapes which are similar to obtained tree structures of estimated trees. Both models describe an evolutionary dynamics and might provide a further opportunity to infer macroevolutionary processes which lead to the biodiversity which can be obtained today. Furthermore with the application of the age model in the context of coevolution by generating a useful benchmark set of cophylogenies is a first step towards systematic studies on evaluating reconciliation methods.

APA, Harvard, Vancouver, ISO, and other styles

3

Shafi, Ghufran. "Development of roadway link screening criteria for microscale carbon monoxide and particulate matter conformity analyses through application of classification tree model." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28222.

Full text

Abstract:

Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2008.
Committee Chair: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Russell, Armistead.

APA, Harvard, Vancouver, ISO, and other styles

4

Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

Full text

Abstract:

Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in conjunction previously, we use LDA in a new way to reduce the dimensionality involved in the training of HMMs. With both LDA and our extension of HMM, we train classifiers to predict development of Chronic Kidney Disease (CKD) in the near future.

APA, Harvard, Vancouver, ISO, and other styles

5

Shew, Cameron Hunter. "TRANSFERABILITY AND ROBUSTNESS OF PREDICTIVE MODELS TO PROACTIVELY ASSESS REAL-TIME FREEWAY CRASH RISK." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/863.

Full text

Abstract:

This thesis describes the development and evaluation of real-time crash risk assessment models for four freeway corridors, US-101 NB (northbound) and SB (southbound) as well as I-880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop detector data. The analysis techniques adopted for this study are logistic regression and classification trees, which are one of the most common data mining tools. The crash risk assessment models are developed based on a binary classification approach (crash and non-crash outcomes), with traffic parameters measured at surrounding vehicle detection station (VDS) locations as the independent variables. The classification performance assessment methodology accounts for rarity of crashes compared to non-crash cases in the sample instead of the more common pre-specified threshold-based classification. Prior to development of the models, some of the data-related issues such as data cleaning and aggregation were addressed. Based on the modeling efforts, it was found that the turbulence in terms of speed variation is significantly associated with crash risk on the US-101 NB corridor. The models estimated with data from US-101 NB were evaluated based on their classification performance, not only on US-101 NB, but also on the other three freeways for transferability assessment. It was found that the predictive model derived from one freeway can be readily applied to other freeways, although the classification performance decreases. The models which transfer best to other roadways were found to be those that use the least number of VDSs–that is, using one upstream and downstream station rather than two or three. The classification accuracy of the models is discussed in terms of how the models can be used for real-time crash risk assessment, which may be helpful to authorities for freeway segments with newly installed traffic surveillance apparatuses, since the real-time crash risk assessment models from nearby freeways with existing infrastructure would be able to provide a reasonable estimate of crash risk. These models can also be applied for developing and testing variable speed limits (VSLs) and ramp metering strategies that proactively attempt to reduce crash risk. The robustness of the model output is assessed by location, time of day and day of week. The analysis shows that on some locations the models may require further learning due to higher than expected false positive (e.g., the I-680/I-280 interchange on US-101 NB) or false negative rates. The approach for post-processing the results from the model provides ideas to refine the model prior to or during the implementation.

APA, Harvard, Vancouver, ISO, and other styles

6

Motloung, Rethabile Frangenie. "Understanding current and potential distribution of Australian acacia species in southern Africa." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79720.

Full text

Abstract:

This dissertation presents research on the value of using different sources of data to explore the factors determining invasiveness of introduced species. The research draws upon the availability of data on the historical trial plantings of alien species and other sources. The focus of the study is on Australian Acacia species as a taxon introduced into southern Africa (Lesotho, South Africa and Swaziland). The first component of the study focused on understanding the factors determining introduction outcome of species in historical trial plantings and invasion success of Australian Acacia species using Species Distribution Models (SDMs) and classification tree techniques. SDMs were calibrated using the native range occurrence records (Australia) and were validated using results of 150 years of South African government forestry trial planting records and invaded range data from the Southern African Plant Invaders Atlas. To understand factors associated with survival (‘trial success’) or failure to survive (‘trial failure’) of species in historical trial plantings, classification and regression tree analysis was used. The results indicate climate as one of the factors that explains introduction and/or invasion success of Australian Acacia species in southern Africa. However, the results also indicate that for ‘trial failures’ there are factors other than climate that could have influenced the trial outcome. This study emphasizes the need to integrate data on whether the species has been recorded to be invasive elsewhere with climate matching for invasion risk assessment. The second component of the study focused on understanding the distribution patterns of Australian Acacia species that are not known as invasive in southern Africa. The specific aims were to determine which species still exist at previously recorded sites and determine the current invasion status. This was done by collating data from different sources that list species introduced into southern Africa and then conducting revisits. For the purpose of this study, revisits means conducting field surveys based on recorded occurrences of introduced species. The known occurrence data for species on the list were obtained from different data sources and various invasion biology experts. As it was not practical to do revisits for all species on the list, three ornamental species (Acacia floribunda, A. pendula and A. retinodes) were selected as part of the pilot study for the conducted revisits in this study. Acacia retinodes trees were not found during the revisits. The results provided data that could be used to characterize species based on the Blackburn et al., (2011) scheme. However, it is not clear whether observed Acacia pendula or A. floribunda trees will spread away from the sites hence the need to continuously monitor sites for spread. The methods used in this research establish a protocol for future work on conducting revisits at known localities of introduced species to determine their population dynamics and thereby characterize the species according to the scheme for management purposes.
Dissertation (MSc)--University of Pretoria, 2014.
National Research Foundation (NRF)
Zoology and Entomology
MSc (Zoology)
Unrestricted

APA, Harvard, Vancouver, ISO, and other styles

7

Mugodo, James, and n/a. "Plant species rarity and data restriction influence the prediction success of species distribution models." University of Canberra. Resource, Environmental & Heritage Sciences, 2002. http://erl.canberra.edu.au./public/adt-AUC20050530.112801.

Full text

Abstract:

There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environmental variables (mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature, mean maximum summer temperature, mean minimum winter temperature, mean daily radiation, mean daily summer radiation, mean daily June radiation, lithology and topography) were used to model the distribution of each of the plant species in the study area. Model predictive performance was measured as the area under the curve of a receiver operating characteristic (ROC) plot. The initial predictive performance of logistic regression models and generalised additive models (GAMs) using unrestricted, temperature restricted, major gradient restricted and climatic domain restricted data gave results that were contrary to current practice in species distribution modelling. Although climatic domain restriction has been used in other studies, it was found to produce models that had the lowest predictive performance. The performance of domain restricted models was significantly (p = 0.007) inferior to the performance of major gradient restricted models when the predictions of the models were confined to the climatic domain of the species. Furthermore, the effect of data restriction on model predictive performance was found to depend on the species as shown by a significant interaction between species and data restriction treatment (p = 0.013). As found in other studies however, the predictive performance of GAM was significantly (p = 0.003) better than that of logistic regression. The superiority of GAM over logistic regression was unaffected by different data restriction regimes and was not significantly different within species. The logistic regression models used in the initial performance comparisons were based on models developed using the forward selection procedure in a rigorous-fitting model-building framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding framework involved testing for the significant reduction in model deviance (p = 0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter estimates and their standard errors were inspected because large estimates and/or standard errors are an indication of model degradation from overfilling or effecls such as mullicollinearily. For additional variables to be included in a model, they had to contribule significantly (p = 0.025) to the model prediclive performance. An attempt to improve the performance of species distribution models using logistic regression models in a rigorousfitting model-building framework, the backward elimination procedure was employed for model selection, bul it yielded models with reduced performance. A liberal-filling model-building framework that used significant model deviance reduction at p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the major criterion for variable selection was employed for the development of logistic regression models using the forward selection and backward elimination procedures. Liberal filling yielded models that had a significantly greater predictive performance than the rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the former models was comparable to that of GAM and classification tree models (CTMs). The low significance liberal-filling models had a much larger number of variables than the high significance liberal-fitting models, but with no significant increase in predictive performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size of a full CTM for a given species. The 10-fold cross-validated model deviance for the subtrees was plotted against the size of the subtree as a means of selecting an appropriate tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance. Species geographical range and species prevalence within the study area were used to categorise the tree species into different distributional forms. These were then used, to compare the effect of plant species rarity on the predictive performance of logistic regression models, GAMs and CTMs. The distributional forms included restricted and rare (RR) species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC) species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides), widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC) species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were significant differences (p = 0.076) in predictive performance among the distributional forms for the logistic regression and GAM. The predictive performance for the WR distributional form was significantly lower than the performance for the other plant species distributional forms. The predictive performance for the RC and RR distributional forms was significantly greater than the performance for the WC distributional form. The trend in model predictive performance among plant species distributional forms was similar for CTMs except that the CTMs had poor predictive performance for the RR distributional form. This study shows the importance of data restriction to model predictive performance with major gradient data restriction being recommended for consistently high performance. Given the appropriate model selection strategy, logistic regression, GAM and CTM have similar predictive performance. Logistic regression requires a high significance liberal-fitting strategy to both maximise its predictive performance and to select a relatively small model that could be useful for framing future ecological hypotheses about the distribution of individual plant species. The results for the modelling of plant species for conservation purposes were encouraging since logistic regression and GAM performed well for the restricted and rare species, which are usually of greater conservation concern.

APA, Harvard, Vancouver, ISO, and other styles

8

Lazaridès, Ariane. "Classification trees for acoustic models : variations on a theme." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0016/MQ37139.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Löwe, Rakel, and Ida Schneider. "Automatic Differential Diagnosis Model of Patients with Parkinsonian Syndrome : A model using multiple linear regression and classification tree learning." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413638.

Full text

Abstract:

Parkinsonian syndrome is an umbrella term including several diseases with similar symptoms. PET images are key when differential diagnosing patients with parkinsonsian syndrome. In this work two automatic diagnosing models are developed and evaluated, with PET images as input, and a diagnosis as output. The two devoloped models are evaluated based on performance, in terms of sensitivity, specificity and misclassification error. The models consists of 1) regression model and 2) either a decision tree or a random forest. Two coefficients, alpha and beta, are introduced to train and test the models. The coefficients are the output from the regression model. They are calculated with multiple linear regression, with the patient images as dependent variables, and mean images of four patient groups as explanatory variables. The coefficients are the underlying relationship between the two. The four patient groups consisted of 18 healthy controls, 21 patients with Parkinson's disease, 17 patients with dementia with Lewi bodies and 15 patients with vascular parkinsonism. The models predict the patients with misclassification errors of 27% for the decision tree and 34% for the random forest. The patient group which is easiest to classify according to both models is healthy controls. The patient group which is hardest to classify is vascular parkinsonism. These results implies that alpha and beta are interesting outcomes from PET scans, and could, after further development of the model, be used as a guide when diagnosing in the models developed.

APA, Harvard, Vancouver, ISO, and other styles

10

Purcell, Terence S. "The use of classification trees to characterize the attrition process for Army manpower models." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA336747.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Classification tree models"

1

Purcell, Terence S. The use of classification trees to characterize the attrition process for Army manpower models. Monterey, Calif: Naval Postgraduate School, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

The Use of Classification Trees to Characterize the Attrition Process for Army Manpower Models. Storming Media, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Shorter, Edward, and Max Fink. Karl Kahlbaum. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190881191.003.0003.

Full text

Abstract:

In 1874, Karl Kahlbaum, a German psychiatrist in an obscure private hospital, pulled various symptom pictures together into a single diagnosis: “catatonia.” Kahlbaum had earlier pioneered the modern classification of illness with his concepts of course and outcome as demarcating the various disease entities. He thought that, similar to neurosyphilis, catatonia had a common cause and common clinical course but, unlike neurosyphilis, often a relatively benign outcome. He believed the illness progressed in fixed stages. At the same time, Kahlbaum’s associate, Ewald Hecker, described madness in young people (“hebephrenia”), which became the forbearer of “schizophrenia.” Kahlbaum’s ideas were not immediately accepted: there was a core of true believers, but many psychiatrists in the Atlantic community did not readily take up the diagnosis and remained skeptical that Kahlbaum had done anything other than repackage familiar symptoms in a new and unfamiliar box.

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Classification tree models"

1

Margineantu, Dragos D., and Thomas G. Dietterich. "Improved Class Probability Estimates from Decision Tree Models." In Nonlinear Estimation and Classification, 173–88. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Suthaharan, Shan. "Decision Tree Learning." In Machine Learning Models and Algorithms for Big Data Classification, 237–69. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Suthaharan, Shan. "Chandelier Decision Tree." In Machine Learning Models and Algorithms for Big Data Classification, 309–28. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Cappelli, Carmela, Francesco Mola, and Roberta Siciliano. "Selecting Regression Tree Models: a Statistical Testing Procedure1." In Advances in Classification and Data Analysis, 249–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59471-7_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Gatnar, Eugeniusz. "Tree-based Models in Statistics: Three Decades of Research." In Classification, Clustering, and Data Analysis, 399–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_44.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Ramírez-Corona, Mallinali, L. Enrique Sucar, and Eduardo F. Morales. "Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies." In Probabilistic Graphical Models, 409–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_27.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Fiasché, Maurizio. "SVM Tree for Personalized Transductive Learning in Bioinformatics Classification Problems." In Recent Advances of Neural Network Models and Applications, 223–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04129-2_22.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Yi, and Taghi M. Khoshgoftaar. "Building Decision Tree Software Quality Classification Models Using Genetic Programming." In Genetic and Evolutionary Computation — GECCO 2003, 1808–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45110-2_75.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Dutt, Rohit, Harish Dureja, and A. K. Madan. "Classification Models Using Decision Tree, Random Forest, and Moving Average Analysis." In New Frontiers in Nanochemistry, 91–115. Includes bibliographical references and indexes. | Contents: Volume 1. Structural nanochemistry – Volume 2. Topological nanochemistry – Volume 3. Sustainable nanochemistry.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429022951-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Gal-Or, Mordechai, Jerrold H. May, and William E. Spangler. "Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models." In Multiple Classifier Systems, 186–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_19.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Classification tree models"

1

Gupta, Mahendra, and S. Minz. "Spatial data classification using decision tree models." In 2017 Conference on Information and Communication Technology (CICT). IEEE, 2017. http://dx.doi.org/10.1109/infocomtech.2017.8340605.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Salama, Khalid M., and Fernando E. B. Otero. "Learning Multi-tree Classification Models with Ant Colony Optimization." In International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005071300380048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Romberg, J., Hyeokho Choi, R. Baraniuk, and N. Kingbury. "Multiscale classification using complex wavelets and hidden Markov tree models." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899396.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Yasser, Khaled, and Elsayed Hemayed. "Location category classification using tree based models with novelty discrimination." In 2017 13th International Computer Engineering Conference (ICENCO). IEEE, 2017. http://dx.doi.org/10.1109/icenco.2017.8289799.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Zhuowen Tu. "Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.194.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

"Using a Fuzzy Decision Tree Ensemble for Tumor Classification from Gene Expression Data." In Special Session on Computational Models based on Soft Computing and its Applications. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004658203200331.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Wang, Zhenghai, Guangdao Hu, YongZhang Zhou, and Xin Liu. "A classification model of Hyperion image base on SAM combined decision tree." In Geoinformatics 2008 and Joint Conference on GIS and Built environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2009. http://dx.doi.org/10.1117/12.813161.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Larasati, Aisyah, Muhammad Farhan, Puji Rahmawati, Nabila Azzahra, Apif Miftahul Hajji, and Anik Nur Handayani. "Designing Classification Models of Patron Visits to an Academic Library using Decision Tree." In Proceedings of the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoemis-19.2019.20.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

To, Cuong, Tuan D. Pham, Tuan Pham, and Xiaobo Zhou. "Binary Classification using Decision Tree based Genetic Programming and Its Application to Analysis of Bio-mass Data." In 2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-09). AIP, 2010. http://dx.doi.org/10.1063/1.3314262.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Fadaie, Gholamreza. "The Influence of Classification on World View and Epistemology." In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3279.

Full text

Abstract:

Worldview as a kind of man's look towards the world of reality has a severe influence on his classification of knowledge. In other words one may see in classification of knowledge the unity as well as plurality. This article deals with the fact that how classification takes place in man's epistemological process. Perception and epistemology are mentioned as the key points here. Philosophers are usually classifiers and their point of views forms the way they classify things and concepts. Relationship and how one looks at it in shaping the classification scheme is critical. The classifications which have been introduced up to now have had several models. They represent the kind of looking at, or point of view of their founders to the world. Aristotle, as a philosopher as well as an encyclopedist, is one of the great founders of knowledge classification. Afterwards the Islamic scholars followed him while some few rejected his model and made some new ones. If we divide all classifications according to their roots we may define them as human based classification, theology based classification, knowledge based classification, materialistic based classification such as Britannica's classification, and fact based classification. Tow broad approaches have been defined in this article: static and dynamic. The static approach refers to the traditional approaches and the dynamic one refers to the eight way of looking toward objects in order to realize them. The structure of classification has had its influence on epistemology, too. If the first cut on knowledge tree is fully defined, the branches would usually be consistent with it.

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Classification tree models"

1

Asher, Sam, Denis Nekipelov, Paul Novosad, and Stephen Ryan. Classification Trees for Heterogeneous Moment-Based Models. Cambridge, MA: National Bureau of Economic Research, December 2016. http://dx.doi.org/10.3386/w22976.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!