Advances in Artificial Intelligence

Research Article

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Table 2

Performance comparison of linear MIL-TWSVM with different dissimilarity score.
DatasetsMax-Hausdorff 
,  
Acc ± std (%)		Min-Hausdorff 
,  
Acc ± std (%)		Average-
Hausdorff 
,  
Acc ± std (%)		City block 
,  
Acc ± std (%)		Chi-squared 
,  
Acc ± std (%)		Mahalanobis 
,  
Acc ± std (%)		EMD 
,  
Acc ± std (%)
Musk 1100, 100 
95.56 ± 2.34		10−2, 10−3 
96.67 ± 5.09		101, 101 
77.78 ± 0.0		100, 103 
61.11 ± 7.85		10−2, 10−2 
64.48 ± 4.84		10−4, 10−4 
70.03 ± 4.58		10−2, 10−2 
75.58 ± 8.31
Musk 210−3, 10−4 
93.02 ± 4.56		10−2, 10−1 
92.85 ± 4.77		10−3, 10−1 
79.84 ± 2.26		10−4, 10−1 
62.46 ± 4.67		10−2, 10−3 
66.67 ± 4.18		10−2, 10−1 
72.34 ± 4.28		10−1, 100 
74.47 ± 7.23
Mutagenesis-atoms100, 10−1 
86.84 ± 1.52		10−1, 10−3 
87.04 ± 2.63		10−2, 10−1 
68.42 ± 0.0		10−1, 10−1 
68.42 ± 0.0		10−2, 10−1 
68.42 ± 0.0		10−2, 100 
68.42 ± 0.0		10−3, 10−3 
87.36 ± 2.57
Winter wren10−4, 10−2 
96.84 ± 2.86		10−1, 10−3 
97.47 ± 2.6		10−4, 10−2 
89.22 ± 5.88		10−3, 100 
79.67 ± 6.56		101, 10−3 
84.26 ± 4.02		10−4, 10−3 
76.89 ± 5.35		10−3, 10−1 
94.71 ± 4.53
Brown creeper10−5, 10−3 
96.01 ± 3.66		10−3, 10−4 
95.88 ± 3.24		10−4, 10−1 
90.89 ± 4.22		10−1, 10−3 
76.38 ± 3.63		10−2, 10−4 
85.43 ± 5.83		10−2, 10−3 
82.67 ± 5.56		10−4, 100 
93.45 ± 5.15
Elephant101, 10−3 
83.88 ± 3.01		10−5, 10−1 
85.56 ± 5.49		100, 100 
55 ± 4.08		100, 100 
50 ± 0.0		10−1, 10−1 
64.16 ± 13.07		100, 100 
50 ± 0.0		10−1, 10−1 
74 ± 5.83
Fox10−4, 10−5 
58.33 ± 1.74		10−3, 102 
67.22 ± 6.7		10−4, 101 
54.44 ± 3.5		10−1, 10−1 
50 ± 0.0		10−2, 10−3 
55 ± 0.0		10−1, 100 
51.5 ± 1.97		10−7, 102 
60 ± 6.32
Tiger10−4, 102 
83.5 ± 2.45		10−6, 10−2 
86.25 ± 5.89		10−5, 10−2 
55 ± 7.28		100, 10−2 
50 ± 0.0		10−3, 10−2 
51.5 ± 4.27		10−1, 10−2 
50 ± 0.0		10−6, 10−5 
74.5 ± 6.5
eastWest10−5, 10−1 
60 ± 20		10−3, 100 
80 ± 24.49		10−1, 100 
80 ± 24.49		10−2, 10−2 
50 ± 0.0		10−1, 100 
50 ± 0.0		10−4, 100 
65 ± 22.9		10−3, 10−1 
80 ± 24.49
westEast100, 10−2 
75 ± 25		100, 100 
90 ± 20		100, 10−3 
85 ± 22.9		100, 100 
50 ± 0.0		10−2, 100 
50 ± 0.0		10−1, 10−1 
75 ± 25		10−2, 10−1 
75 ± 25