Ensemble Classification Methods with Applications in R

Inbunden, Engelska, 2018

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Beskrivning

An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods’ basic characteristics and explain the types of problems that can emerge in its application.Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: Offers an important text that has been tested both in the classroom and at tutorials at conferencesContains authoritative information written by leading experts in the fieldPresents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification treesWritten for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.

Produktinformation

Utgivningsdatum:2018-10-26
Mått:168 x 246 x 18 mm
Vikt:499 g
Format:Inbunden
Språk:Engelska
Antal sidor:224
Förlag:John Wiley & Sons Inc
ISBN:9781119421092

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

ESTEBAN ALFARO, MATÍAS GÁMEZ AND NOELIA GARCÍA are Associate Professors at the Applied Economics Department (Statistics), Faculty of Economics and Business of Albacete, and researchers at the Regional Development Institute (IDR), University of Castilla-La Mancha. Together they have published several papers in prestigious journals on topics such as applications of ensemble trees to corporate bankruptcy, credit scoring and statistical quality control with the most notable in Journal of Statistical Software, Vol 54.

Innehållsförteckning

List of Contributors ixList of Tables xiList of Figures xvPreface xvii1 Introduction 1Esteban Alfaro, Matías Gámez, and Noelia García1.1 Introduction 11.2 Definition 11.3 Taxonomy of Supervised Classification Methods 21.4 Estimation of the Accuracy of a Classification System 31.4.1 The Apparent Error Rate 41.4.2 Estimation of the True Error Rate 41.4.3 Error Rate Estimation Methods 41.4.4 The Standard Error 61.5 Classification Trees 71.5.1 Classification Tree Building 81.5.2 Splitting Rule 91.5.3 Splitting Criteria 101.5.4 Goodness of a Split 101.5.5 The Impurity of a Tree 111.5.6 Stopping Criteria 111.5.7 Overfitting in Classification Trees 121.5.8 Pruning Rules 142 Limitation of the Individual Classifiers 19Esteban Alfaro, Matías Gámez, and Noelia García2.1 Introduction 192.2 Error Decomposition: Bias and Variance 202.3 Study of Classifier Instability 232.4 Advantages of Ensemble Classifiers 262.5 Bayesian Perspective of Ensemble Classifiers 283 Ensemble Classifiers Methods 31Esteban Alfaro, Matías Gámez, and Noelia García3.1 Introduction 313.2 Taxonomy of Ensemble Methods 323.2.1 Non-Generative Methods 333.2.2 Generative Methods 333.3 Bagging 343.4 Boosting 363.4.1 AdaBoost Training Error 403.4.2 AdaBoost and the MarginTheory 413.4.3 Other Boosting Versions 433.4.4 Comparing Bagging and Boosting 463.5 Random Forests 464 Classification with Individual and Ensemble Trees in R 51Esteban Alfaro, Matías Gámez, and Noelia García4.1 Introduction 514.2 adabag: An R Package for Classification with Boosting and Bagging 524.2.1 The bagging, predict.bagging, and bagging.cv Functions 564.2.2 The boosting, predict.boosting, and boosting.cv Functions 654.2.3 The margins, plot.margins, errorevol and plot.errorevol Functions 714.2.4 The MarginOrderedPruning.Bagging Function 754.3 The “German Credit” Example 794.3.1 Classification Tree 814.3.2 Combination using Bagging 854.3.3 Combination using Boosting 884.3.4 Combination using Random Forest 904.3.5 Cross-Validation Comparison 955 Bankruptcy Prediction Through Ensemble Trees 97Esteban Alfaro, Matías Gámez, and Noelia García5.1 Introduction 975.2 Problem Description 975.3 Applications 995.3.1 The Dichotomous Case 995.3.2 TheThree-Class Case 1115.4 Conclusions 1176 Experiments with Adabag in Biology Classification Tasks 119M. Fernández-Delgado, E. Cernadas, and M. Pérez-Ortiz6.1 Classification of Color Texture Feature Patterns Extracted From Cells in Histological Images of Fish Ovary 1196.2 Direct Kernel Perceptron: Ultra-Fast Kernel ELM-Based Classification with Non-Iterative Closed-Form Weight Calculation 1226.3 Do We Need Hundreds of Classifiers to Solve Real-World Classification Problems? 1256.4 On the use of Nominal and Ordinal Classifiers for the Discrimination of Stages of Development in Fish Oocytes 1297 Generalization Bounds for Ranking Algorithms 135W. Rejchel7.1 Introduction 1357.2 Assumptions, Main Theorem, and Application 1367.3 Experiments 1387.4 Conclusions 1398 Classification and Regression Trees for Analyzing Irrigation Decisions 141S. Andriyas andM.McKee8.1 Introduction 1418.2 Theory 1438.3 Case Study and Methods 1448.3.1 Study Site and Data Available 1448.3.2 Model, Specifications, and Performance Evaluation 1468.4 Results and Discussion 1478.5 Conclusions 1539 Boosted Rule Learner and its Properties 155M. Kubus9.1 Introduction 1559.2 Separate-and-Conquer 1569.3 Boosting in Rule Induction 1579.4 Experiments 1589.5 Conclusions 16110 Credit Scoring with Individuals and Ensemble Trees 163M. Chrzanowska, E. Alfaro, and D.Witkowska10.1 Introduction 16310.2 Measures of Accuracy 16410.3 Data Description 16510.4 Classification of Borrowers Applying Ensemble Trees 16810.5 Conclusions 17311 An Overview of Multiple Classifier Systems Based on Generalized Additive Models 175K.W. De Bock, K. Coussement, and D. Cielen11.1 Introduction 17511.2 Multiple Classifier Systems Based on GAMs 17611.2.1 Generalized AdditiveModels 17611.2.2 GAM-Based Multiple Classifier Systems 17711.2.3 GAMensPlus: Extending GAMens for Advanced Interpretability 17911.3 Experiments and Applications 18011.3.1 A Multi-Domain Benchmark Study of GAM-Based Ensemble Classifiers 18011.3.2 Benchmarking GAM-Based Ensemble Classifiers in Predictive Customer Analytics 18111.3.3 A Case Study of GAMensPlus used for Customer Churn Prediction in Financial Services 18311.4 Software Implementation in R: the GAMens Package 18511.5 Conclusions 185References 187Index 197