featureselectexhaustive02.txt

这是台湾张智星的模式识别的课件的源码

Construct 63 KNN models, each with up to 6 inputs selected from 6 candidates...
modelIndex 1/63: 1 --> Recognition rate = 42.500000%
modelIndex 2/63: 2 --> Recognition rate = 46.000000%
modelIndex 3/63: 3 --> Recognition rate = 56.000000%
modelIndex 4/63: 4 --> Recognition rate = 41.500000%
modelIndex 5/63: 5 --> Recognition rate = 31.500000%
modelIndex 6/63: 6 --> Recognition rate = 28.000000%
modelIndex 7/63: 1, 2 --> Recognition rate = 73.000000%
modelIndex 8/63: 1, 3 --> Recognition rate = 71.500000%
modelIndex 9/63: 1, 4 --> Recognition rate = 67.500000%
modelIndex 10/63: 1, 5 --> Recognition rate = 49.000000%
modelIndex 11/63: 1, 6 --> Recognition rate = 46.500000%
modelIndex 12/63: 2, 3 --> Recognition rate = 74.500000%
modelIndex 13/63: 2, 4 --> Recognition rate = 66.000000%
modelIndex 14/63: 2, 5 --> Recognition rate = 50.500000%
modelIndex 15/63: 2, 6 --> Recognition rate = 49.000000%
modelIndex 16/63: 3, 4 --> Recognition rate = 70.000000%
modelIndex 17/63: 3, 5 --> Recognition rate = 45.500000%
modelIndex 18/63: 3, 6 --> Recognition rate = 49.000000%
modelIndex 19/63: 4, 5 --> Recognition rate = 46.500000%
modelIndex 20/63: 4, 6 --> Recognition rate = 45.000000%
modelIndex 21/63: 5, 6 --> Recognition rate = 31.000000%
modelIndex 22/63: 1, 2, 3 --> Recognition rate = 92.500000%
modelIndex 23/63: 1, 2, 4 --> Recognition rate = 94.500000%
modelIndex 24/63: 1, 2, 5 --> Recognition rate = 72.000000%
modelIndex 25/63: 1, 2, 6 --> Recognition rate = 72.500000%
modelIndex 26/63: 1, 3, 4 --> Recognition rate = 92.500000%
modelIndex 27/63: 1, 3, 5 --> Recognition rate = 70.000000%
modelIndex 28/63: 1, 3, 6 --> Recognition rate = 68.500000%
modelIndex 29/63: 1, 4, 5 --> Recognition rate = 67.500000%
modelIndex 30/63: 1, 4, 6 --> Recognition rate = 68.500000%
modelIndex 31/63: 1, 5, 6 --> Recognition rate = 47.500000%
modelIndex 32/63: 2, 3, 4 --> Recognition rate = 92.500000%
modelIndex 33/63: 2, 3, 5 --> Recognition rate = 74.500000%
modelIndex 34/63: 2, 3, 6 --> Recognition rate = 70.500000%
modelIndex 35/63: 2, 4, 5 --> Recognition rate = 66.000000%
modelIndex 36/63: 2, 4, 6 --> Recognition rate = 67.500000%
modelIndex 37/63: 2, 5, 6 --> Recognition rate = 51.000000%
modelIndex 38/63: 3, 4, 5 --> Recognition rate = 69.500000%
modelIndex 39/63: 3, 4, 6 --> Recognition rate = 75.000000%
modelIndex 40/63: 3, 5, 6 --> Recognition rate = 55.500000%
modelIndex 41/63: 4, 5, 6 --> Recognition rate = 47.000000%
modelIndex 42/63: 1, 2, 3, 4 --> Recognition rate = 99.000000%
modelIndex 43/63: 1, 2, 3, 5 --> Recognition rate = 92.500000%
modelIndex 44/63: 1, 2, 3, 6 --> Recognition rate = 93.500000%
modelIndex 45/63: 1, 2, 4, 5 --> Recognition rate = 92.500000%
modelIndex 46/63: 1, 2, 4, 6 --> Recognition rate = 94.500000%
modelIndex 47/63: 1, 2, 5, 6 --> Recognition rate = 76.000000%
modelIndex 48/63: 1, 3, 4, 5 --> Recognition rate = 92.500000%
modelIndex 49/63: 1, 3, 4, 6 --> Recognition rate = 91.000000%
modelIndex 50/63: 1, 3, 5, 6 --> Recognition rate = 68.000000%
modelIndex 51/63: 1, 4, 5, 6 --> Recognition rate = 67.500000%
modelIndex 52/63: 2, 3, 4, 5 --> Recognition rate = 91.500000%
modelIndex 53/63: 2, 3, 4, 6 --> Recognition rate = 91.000000%
modelIndex 54/63: 2, 3, 5, 6 --> Recognition rate = 72.500000%
modelIndex 55/63: 2, 4, 5, 6 --> Recognition rate = 66.500000%
modelIndex 56/63: 3, 4, 5, 6 --> Recognition rate = 70.500000%
modelIndex 57/63: 1, 2, 3, 4, 5 --> Recognition rate = 97.000000%
modelIndex 58/63: 1, 2, 3, 4, 6 --> Recognition rate = 98.500000%
modelIndex 59/63: 1, 2, 3, 5, 6 --> Recognition rate = 91.500000%
modelIndex 60/63: 1, 2, 4, 5, 6 --> Recognition rate = 93.000000%
modelIndex 61/63: 1, 3, 4, 5, 6 --> Recognition rate = 92.000000%
modelIndex 62/63: 2, 3, 4, 5, 6 --> Recognition rate = 91.000000%
modelIndex 63/63: 1, 2, 3, 4, 5, 6 --> Recognition rate = 98.500000%

Overall max recognition rate = 99.0%.
Overall selected inputs: 1, 2, 3, 4