Feature subset selection for classification of histological images

被引:20
作者
Jelonek, J
Stefanowski, J
机构
[1] Institute of Computing Science, Poznan University of Technology, 60-965 Poznan
关键词
classification systems; case-based learning; feature selection; histopathology;
D O I
10.1016/S0933-3657(96)00375-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classification of histological images is considered in this paper. The task is to distinguish different classes of tumours of the central nervous system on the basis of features extracted from microscopic slides. The number of extracted features is relatively high and some of them seem to be irrelevant for classification of the images. Thus, the main objective of this study is to select such a feature subset that improves the predictive accuracy of the classifier. The wrapper approach is chosen to obtain this aim, where a search for the good subset of features is made using a non-parametric case-base classifier. To guide a search process, a forward beam selection algorithm is introduced. It sequentially adds relevant features in a parallel way for the most promising subsets. It is shown that the proposed approach gives good predictive accuracy for the considered histopathological problem. (C) 1997 Elsevier Science B.V.
引用
收藏
页码:227 / 239
页数:13
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