Creating Ensemble Classifiers with Information Entropy Diversity Measure

Author:

Zou Jiangbo12,Fu Xiaokang34,Guo Lingling5,Ju Chunhua3,Chen Jingjing6ORCID

Affiliation:

1. Chinese Academy of International Trade and Economic Cooperation, Beijing 10071, China

2. School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China

3. School of E-Commerce & Management Science, Zhejiang Gongshang University, Hangzhou 310018, China

4. Sunyard System Engineering Co., Ltd., Hangzhou 310053, China

5. College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

6. Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China

Abstract

Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity of its component classifiers. In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from base classifiers, in which the number of component classifiers is dynamically adjusted to minimize system cost. It is demonstrated that our method has an obvious lower memory cost with higher classification accuracy compared with existing classifier methods.

Funder

Zhejiang Gongshang University

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

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