Ensemble Classification Methods with Applications in R
by: Esteban Alfaro
ISBN-10: 1119421098
ISBN-13: 9781119421092
Edition 版次: 1
Publication Date 出版日期: 2018-11-05
Print Length 页数: 224 pages
Book Description
By finelybook
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.
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
EULA
