Ensemble Classification Methods with Applications in R

Ensemble Classification Methods with Applications in R

by Esteban Alfaro, Matías Gámez, Noelia García
Released November 2018
Publisher(s): Wiley
ISBN: 9781119421092

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Book description

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 conferences
  • Contains authoritative information written by leading experts in the field
  • Presents 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 trees

Written 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.

 

Table of contents

  1. Cover
  2. List of Contributors
  3. List of Tables
  4. List of Figures
  5. Preface
  6. Chapter 1: Introduction
    1. 1.1 Introduction
    2. 1.2 Definition
    3. 1.3 Taxonomy of Supervised Classification Methods
    4. 1.4 Estimation of the Accuracy of a Classification System
    5. 1.5 Classification Trees
  7. Chapter 2: Limitation of the Individual Classifiers
    1. 2.1 Introduction
    2. 2.2 Error Decomposition: Bias and Variance
    3. 2.3 Study of Classifier Instability
    4. 2.4 Advantages of Ensemble Classifiers
    5. 2.5 Bayesian Perspective of Ensemble Classifiers
  8. Chapter 3: Ensemble Classifiers Methods
    1. 3.1 Introduction
    2. 3.2 Taxonomy of Ensemble Methods
    3. 3.3 Bagging
    4. 3.4 Boosting
    5. 3.5 Random Forests
  9. Chapter 4: Classification with Individual and Ensemble Trees in R
    1. 4.1 Introduction
    2. 4.2 adabag: An R Package for Classification with Boosting and Bagging
    3. 4.3 The “German Credit” Example
  10. Chapter 5: Bankruptcy Prediction Through Ensemble Trees
    1. 5.1 Introduction
    2. 5.2 Problem Description
    3. 5.3 Applications
    4. 5.4 Conclusions
  11. Chapter 6: Experiments with Adabag in Biology Classification Tasks
    1. 6.1 Classification of Color Texture Feature Patterns Extracted From Cells in Histological Images of Fish Ovary
    2. 6.2 Direct Kernel Perceptron: Ultra‐Fast Kernel ELM‐Based Classification with Non‐Iterative Closed‐Form Weight Calculation
    3. 6.3 Do We Need Hundreds of Classifiers to Solve Real‐World Classification Problems?
    4. 6.4 On the use of Nominal and Ordinal Classifiers for the Discrimination of Stages of Development in Fish Oocytes
  12. Chapter 7: Generalization Bounds for Ranking Algorithms
    1. 7.1 Introduction
    2. 7.2 Assumptions, Main Theorem, and Application
    3. 7.3 Experiments
    4. 7.4 Conclusions
  13. Chapter 8: Classification and Regression Trees for Analyzing Irrigation Decisions
    1. 8.1 Introduction
    2. 8.2 Theory
    3. 8.3 Case Study and Methods
    4. 8.4 Results and Discussion
    5. 8.5 Conclusions
  14. Chapter 9: Boosted Rule Learner and its Properties
    1. 9.1 Introduction
    2. 9.2 Separate‐and‐Conquer
    3. 9.3 Boosting in Rule Induction
    4. 9.4 Experiments
    5. 9.5 Conclusions
  15. Chapter 10: Credit Scoring with Individuals and Ensemble Trees
    1. 10.1 Introduction
    2. 10.2 Measures of Accuracy
    3. 10.3 Data Description
    4. 10.4 Classification of Borrowers Applying Ensemble Trees
    5. 10.5 Conclusions
  16. Chapter 11: An Overview of Multiple Classifier Systems Based on Generalized Additive Models
    1. 11.1 Introduction
    2. 11.2 Multiple Classifier Systems Based on GAMs
    3. 11.3 Experiments and Applications
    4. 11.4 Software Implementation in R: the GAMens Package
    5. 11.5 Conclusions
  17. References
  18. Index
  19. End User License Agreement

Product information

  • Title: Ensemble Classification Methods with Applications in R
  • Author(s): Esteban Alfaro, Matías Gámez, Noelia García
  • Release date: November 2018
  • Publisher(s): Wiley
  • ISBN: 9781119421092