anytime algorithms for learning anytime classifiers saher ... - Technion

Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008
Accuracy
Accuracy
Accuracy
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.8
0.75
0.7
0.65
0.6
0.55
0.5
2000 3000 4000
Tree size
0.45
100 110 120 130 140 150 160
0.7
0.65
0.6
0.55
0.5
Tree size
0.45
320 340 360 380 400 420 440
Tree size
Accuracy
Accuracy
Accuracy
0.96
0.94
0.92
0.9
0.88
0.86
0.84
1000 1500 2000 2500
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
Tree size
0.5
40 45 50 55 60 65 70 75 80
0.85
0.8
0.75
0.7
0.65
0.6
0.55
Tree size
0.5
160 180 200 220 240 260 280 300
Tree size
Accuracy
Accuracy
Accuracy
0.988
0.986
0.984
0.982
0.98
0.978
0.976
0.974
0.972
0.97
550 600 650 700 750 800
0.9
0.85
0.8
0.75
0.7
0.65
Tree size
0.6
34 36 38 40 42 44 46 48
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
Tree size
0.55
20 40 60 80 100 120 140 160
Tree size
Figure B.5: Correlation between size and accuracy using RTG (left-most), SID3
(middle), and LSID3 (right-most). The upper graphs represent the results for the
Nursery dataset, the graphs in the middle row stand for the Glass dataset and the
lower graphs stand for the Multiplexer-20 dataset.
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