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OCR: Table 2. Neurol-nalwork pattern classifiers constructed using the entire sel of features compared with those con- structed using the most accurate subsets selected liy the genetic algorillim. The column labeled accuracy shows the generalization accuracy; and the column labeled hidden shows the number of hidden neurons generated in the neural networks. ALL ATTRIBUTES GA-SELECTED SUBSET DASA SET FEATURES ACCURACY FEATURES ACCURACY (No.) (%) HOODER - (%) HIODER 3P 13 79.0 12.2 5.0# 2.0 6.6 + 1.6 9.2 + 4.9 Annealing 38 100 0.0 $6.6 # 2.0 12:1₺2.4 21.0 + 3.1 99.5 + 0.9 11.1+2.9 Alsdinlogy 69 65.0 £ 9.7 24.7+4.8 36.4 +3:5 83.5+ 8.2 27.4+ 5.6 Bridges 11 5:2 +3.3 5.6 + 1.5 81.6+ 7.6 Cancer 9 63.0 £ 7.8 17.6 + 12.4 97.8 ₺ 1.2 2.9+ 1.2 5.4 + 1.4 99.3 + 0.9 57+2.9 CRX 15 87.7£3.3 7.7 + 6,9 8.0 + 2.1 91.5 + 2.8 12.5₺ 7.6 28 85.8 * 9.5 9.1.+ 6.2 14.0 + 2.6 78.1+7.8 11.2 6.5 Glass 9 70.5 + 8.5 9:8+ 6.9 5.5+ 1.4 80.8+ 5.0 14.5+ 6.6 Heart 13 86.7¥7.6 5.7 ₺ 4.4 7.2 + 1.6 93.9 + 3.8 7.5+ 3.9 HeartCle 13 85.3₺2.7 3.4+ 1.1 7.3+1.7 92.9 + 3.6 7.6₺ 4.2 13 85.9 ₺ 6.3 5.0+2.9 7.0₺ 1.2 93.0 + 4.0 HeartSwi 13 94.2₺ 3.8 6.6 + 1:7 98.3 + 3.3 7.1+3:7 2.2+06 3.7+ 1.5 HeartVa. 13 80.0 + 7.4 5.1+ 2.6 2.1+ 1.7 8.5 # 3.0 Hepatitis 19 91.0+5.7 84.7 + 9.5 6:2+ 4.0 9.2+2.3. 97.1 + 4.3 8.1+2.8 Horse 22 86.0+ 3.6 5.3+4.5 111+2.3 92.6 + 3.4 9.5+ 4.1 onosphere 34 94.3 + 5,0 55 + 1.6 17.3135 98.6 + 2.4 7.5+2.4 Eiver 6 72.9 + 5.1 21.5 + 27.3 4.1+0.7 77.8 4.0 Pima 76.3# 5.1 79.5+3.1 25.9 + 24.3 8 88.0 +.7.5 8.14:4.9 3.8 + 1.5. 20.0 + 21.2 Promoters 2.2 + 0.4 28.8+3.3 100+ 0.0 2.7₺ 1,0 Sona 60 83.0 + 7.8 64+27 30.7 + 3.7 97.2 + 2.9 7.2130 Soybean 35 81.0+ 5.6 20.2 1: 3.2 19.4+ 2.7 92.8 + 5.9 23.3 + 4.3 Vehicle 18 65:4 £ 3.5 23.7 +5.0 9.1 +1.7 68.8 4 4.3 36.2 + 18.2 Votes 16 3.2 +1.5 8.9+ 1:8 98.8 = 1:2 4.0 + 1.8 Vowel 10 96.1 + 1.5 69.8 + 6.4 38.0 # 8.3. 6.5+12 78.4 + 3.8. 41.5+7.7 13 97.1# 4.0 5:5*1.7 67+16 99.4 4: 2.1 5.9+2.1 16 96.0+4.9 61+1.1 9.3 + 1.6 100+ 0.0 6.2+ 1:1