主成分分析を用いた特徴抽出が容易なデータセットの作成

110 2009 10 1 23 2 2.1 (Principal Component Analysis:PCA) PCA 2.1.1 P x p PCA P Z p (p = 1; 2; ; P ) x p a pi (i = 1; 2; ; P ) Z p (1) PXZ p =i=1a pi x i (i = 1; 2; ; P ) (1)2.1.2 0 1 V p (p = 1; 2; ; P ) Z p C p (2) C p =VPpPi=1 V i(2) 1 2 3 2.2 2 80% 3 (3) (4) Pf = i=3 V iP Pi=1 V iNX(3)g i = x i 0:8N (4)i=1 (3) (4) N i(i = 1; 2; ; N) P x i = 1 i x i = 0V i x i 1 3 GA 3.1 (Genetic Algorithm:GA) GA 3.2 N N i(i =1; 2; ; N) i 1 0 1 N 80% 1

3.3 A P A V i (i = 1; 2; ; P ) (5) 3 Pfitness = i=1 V iP Pi=3 V i(5)3.4 80% 1. 2. 3. 0 1 Fig.1GA 3.5 1 1= 4 4.1 UCI 2) wine regognition data 178 13 4.2 Table 1 Table1GA 200 400 0.91= 178 1Fig.2Fig. 2 5 GA 1) ()20052) UCI Machine Learning Repositoryhttp://archive.ics.uci.edu/ml/4.3 Fig. 1 GA Fig. 2 GA 2