A novel Supervised Competitive Learning algorithm

被引：5

作者：

Dai, Qun ^{[1
]}

Song, Gang ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Jiangsu, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 191卷

基金：

中国国家自然科学基金;

关键词：

Competitive learning; Supervised Competitive Learning (SCL) algorithm; Multiple Classifier Systems (MCSs); Ordinary Supervised Learning (OSL) algorithm; Pattern classification; ENSEMBLE PRUNING ALGORITHM; CLASSIFIERS; DIVERSITY; SYSTEM; BUILD;

D O I：

10.1016/j.neucom.2016.01.045

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Competitive learning is a mechanism well-suited for the learning paradigm of regularity detection, and is typically an unsupervised learning mechanism. However, in this work, a novel Supervised Competitive Learning (SCL) algorithm is proposed for the generation of Multiple Classifier Systems (MCSs), which is substantially supervised. SCL algorithm seeks to strengthen simultaneously both the accuracy of and the diversity among the base classifiers in the MCSs, in a supervised and competitive manner. Our inspiration for the development of SCL algorithm comes from the modern education concept and those classical competitive learning algorithms intuitively. It is found through the experimental study of this work that, SCL algorithm effectively improves the classification and generalization performance of the constructed MCSs. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：356 / 362

页数：7

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