Input variable selection for multi-layer neural networks

Junichi Murata, Toru Nakazono, Kotaro Hirasawa

研究成果: ジャーナルへの寄稿学術誌査読


A method is proposed for selecting relevant input variables to multi-layer neural networks. A minimal set of inputs is selected which is necessary to obtain a network with a good generalization ability and some insight into the input-output relationship. The inputs of network are selected automatically by a combination of constructive and destructive algorithms. The constructive algorithm starts with a minimal input set and adds new inputs if necessary, while the destructive algorithm deletes unnecessary inputs. The main issue addressed here is the measure of input significance used in the constructive algorithm. Some measures are proposed based on mutual infomation and linear correlation paying much attention to the structural constraint imposed on the networks. The experimental results show that the measures are valid and that the derived network with the selected inputs has a good generalization ability.

ジャーナルResearch Reports on Information Science and Electrical Engineering of Kyushu University
出版ステータス出版済み - 1998

!!!All Science Journal Classification (ASJC) codes

  • 電子工学および電気工学
  • ハードウェアとアーキテクチャ
  • 工学(その他)


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