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Table 2: Experimental results for UCI machine learning repository. Hyperparameters were tuned with only training data
through cross validation. “Ins.” denotes the number of instances after removing missing data. “Att.” denotes the number
of attributes excluding class attribute, and “#” denotes the number of support vectors or prototypes. The proposed method,
referred to as ILM, was evaluated with different parameter sets: some parameters were fixed as (a)C = 0, p = −2
8
, (b)C = 0,
(c) p = −1, and the other parameters were varied like in Table 1.
SVM ILM
Dataset Ins. Att. (a) (b) (c)
Error # Error # Error # Error #
(%) (%) (%) (%)
BreastCancer 683 9 2.8 66 2.5 32 2.6 64 2.6 64
Cards 653 15 13.6 189 13.2 8 13.9 8 14.6 4
Heart1 297 13 14.5 159 16.5 2 18.2 64 14.8 2
HouseVotes84 435 16 3.7 101 3.7 2 3.9 16 3.7 32
Ionosphere 351 34 4.8 148 8.3 8 3.7 32 4.3 64
Liver 345 6 28.1 203 28.1 4 27.5 4 26.3 4
P.I. Diabetes 768 8 22.3 418 22.5 2 22.3 32 21.9 64
Sonar 208 60 12.1 125 11.1 16 9.6 8 11.5 16
Tictactoe 958 9 1.7 349 1.7 2 0.9 16 1.7 2
conventional nearest neighbor rule. It was shown that
prototypes and biases can be trained simultaneously
by General Loss Minimization, which is a general
framework for classifier design. The preliminary ex-
periments raised the possibility that prototypes having
larger biases can be removed without degrading per-
formance, but this redundancy removal should be in-
vestigated further. Experimental results for UCI ma-
chine learning repository revealed that the proposed
method achieves almost the same as or higher clas-
sification accuracy than SVM for all of nine datasets
with a much fewer prototypes than support vectors.
In future, the proposed method will be evaluated for
various classification problems in real world.
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