.png){kind=icon}
返回
A hybrid feature selection method for DNA microarray data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2011.02.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Feature selection
Taguchi-genetic algorithm
K-nearest neighbor
Leave-one-out cross-validation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
引用论文
分享收藏Particle robotics based on statistical mechanics of loosely coupled components基于松散耦合构件统计力学的粒子机器人技术.png)
Nature
NatureIF0
PREAI
分享收藏
分享收藏分享收藏
查看更多内容