期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 51, 期 8, 页码 883-894出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2010.05.003
关键词
Data classification; Fuzzy sets; Pattern recognition; Fuzzy integrals; Feature selection; Computational intelligence
Classifying magnetic resonance spectra is often difficult due to the curse of dimensionality; scenarios in which a high-dimensional feature space is coupled with a small sample size. We present an aggregation strategy that combines predicted disease states from multiple classifiers using several fuzzy integration variants. Rather than using all input features for each classifier, these multiple classifiers are presented with different, randomly selected, subsets of the spectral features. Results from a set of detailed experiments using this strategy are carefully compared against classification performance benchmarks. We empirically demonstrate that the aggregated predictions are consistently superior to the corresponding prediction from the best individual classifier. Crown Copyright (C) 2010 Published by Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据