Ensemble Classification Methods with Applications in R

Esteban Alfaro, Matías Gámez, Noelia García

• Publisher: Wiley
• Year: 2018
• ISBN:
  9781119421559
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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 inmachine 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.

1
Introduction

Esteban Alfaro Matías Gámez and Noelia García

1.1 Introduction

Classification as a statistical task is present in a wide range of real‐life contexts as diverse as, for example, the mechanical procedure to send letters based on the automatic reading of the postal codes, decisions regarding credit applications from individuals or the preliminary diagnosis of a patient's condition to enable immediate treatment while waiting for the final results of tests.

In its most general form, the term classification can cover any context in which a decision is taken or a prediction is made based on the information available at that time, and a classification procedure is, then, a formal method to repeat the arguments that led to that decision for new situations.

This work focuses on a more specific interpretation. The problem is to build a procedure that will be applied to a set of cases in which each new case has to be assigned to one of a set of predefined classes or subpopulations on the basis of observed characteristics or attributes.

The construction of a classification system from a set of data for which actual classes are known has been called different things, such as pattern recognition, discriminant analysis, or supervised learning. The latter name is used rather than unsupervised learning or clustering in which classes are not defined a priori but they are inferred from the data. This work focuses on the first type of classification tasks.

1.2 Definition

The most traditional statistical technique applied to supervised classification is linear discriminant analysis, but in recent decades a wider set of new methods has been developed, in part due to the improvement in the capabilities of informatics supports. Generally, the performance of a classification procedure is analysed based on its accuracy, that is, the percentage of correct classified cases. The existence of a correct classification implies the existence of an expert or supervisor capable of providing it, so why would we want to replace this exact system by an approximation? Among the reasons for this replacement we could mention:

1. Speed. Automatic procedures are usually quick and they can help to save time. For instance, automatic readers of postal codes are able to read most letters, leaving only some very complex cases to human experts.
2. Objectivity. Important decisions have to be taken basing on objective criteria under the same conditions for all cases. Objectivity is sometimes difficult to ensure in the case of human deciders. In such cases, decisions can be affected from external factors, which would led us to take biased decisions.
3. Explanatory capabilities. Some of the classification methods allow us not only to classify observations but to explain the reasons for the decision in terms of a set of statistical features.
4. Economy. Having an expert who make decisions can be much mor...

Table of contents

• Cover
• Table of Contents
• List of Contributors
• List of Tables
• List of Figures
• Preface
• Chapter 1: Introduction
• Chapter 2: Limitation of the Individual Classifiers
• Chapter 3: Ensemble Classifiers Methods
• Chapter 4: Classification with Individual and Ensemble Trees in R
• Chapter 5: Bankruptcy Prediction Through Ensemble Trees
• Chapter 6: Experiments with Adabag in Biology Classification Tasks
• Chapter 7: Generalization Bounds for Ranking Algorithms
• Chapter 8: Classification and Regression Trees for Analyzing Irrigation Decisions
• Chapter 9: Boosted Rule Learner and its Properties
• Chapter 10: Credit Scoring with Individuals and Ensemble Trees
• Chapter 11: An Overview of Multiple Classifier Systems Based on Generalized Additive Models
• References
• Index
• End User License Agreement