1st Edition
by Esteban Alfaro (Editor), Matías Gámez (Editor), Noelia García (Editor)
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.
