Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials

被引：0
|
作者：
Smolinski, Tomasz G. [1 ]
Boratyn, Grzegorz M. [2 ]
Milanova, Mariofanna [3 ]
Buchanan, Roger [4 ]
Prinz, Astrid A. [1 ]
机构：
[1] Emory Univ, Dept Biol, Atlanta, GA 30322 USA
[2] Univ Louisville, Kidney Dis Program, Louisville, KY 40292 USA
[3] Univ Arkansas, Dept Comp Sci, Little Rock, AR 72204 USA
[4] Arkansas State Univ, Dept Biol, State Univ, AR 72467 USA
来源：
PATTERN RECOGNITION IN BIOINFORMATICS, PROCEEDINGS | 2006年 / 4146卷
基金：
美国国家卫生研究院;
关键词：
D O I：
暂无
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
This article presents a continuation of our research aiming at improving the effectiveness of signal decomposition algorithms by providing them with "classification-awareness." We investigate hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform the task of decomposition in the light of the underlying classification problem itself. In this part of the study, we also investigate the idea of utilizing the Independent Component Analysis (ICA) to initialize the population in the MOEA.
引用
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页码：174 / +
页数：2
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