A new wrapper feature selection approach using neural network

Material type: 文書・図像類

Author: KABIR, Md. Monirulほか

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Publication date: 2010

Material Format: Digital

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Note (General)：: This paper presents a new feature selection(FS)algorithm based on the wrapper approach using neural networks(NNs).The vital aspect of this algorithm i...

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Material Type: 文書・図像類
Title: A new wrapper feature selection approach using neural network
Author/Editor: KABIR, Md. Monirul
ISLAM, Md. Monirul
MURASE, Kazuyuki
Author Heading: KABIR, Md. Monirul
ISLAM, Md. Monirul
MURASE, Kazuyuki
Publication Date: 2010
Publication Date (W3CDTF): 2010
Periodical title: Neurocomputing
No. or year of volume/issue: 73
Volume: 73
Pages: 3273-3283
Text Language Code: eng
Target Audience: 一般
Note (General): This paper presents a new feature selection(FS)algorithm based on the wrapper approach using neural networks(NNs).The vital aspect of this algorithm is the automatic determination of NN architectures during the FS process. Our algorithm uses a constructive approach involving correlation information in selectingf eatures and determining NN architectures. We call this algorithm as constructive approach for FS(CAFS). The aim of using correlation information in CAFS is to encourage the search strategy for selecting less correlated(distinct)features if they enhance accuracy of NNs. Such an encouragement will reduce redundancy of information resulting in compact NN architectures. We evaluate the performance of CAFS on eight benchmark classification problems. The experimental results show the essence of CAFS in selecting features with compact NN architectures.
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ