Abstract：Multiconlitron is a general framework for constructing piecewise linear classifiers. For the convexly separable and the commonly separable datasets, it can correctly separate them by using support conlitron algorithm(SCA) and multiconlition algorithm(SMA), respectively. On this basis, a soft margin method for multiconlitron design is proposed. Firstly, the training samples are mapped from the input space to a high dimensional feature space, and one class of those samples is clustered into some groups by K-means algorithm. Then, the conlitron is constructed between each group and another class of samples, and the integrated model, multiconlitron, is obtained. The proposed method can overcome the inapplicability of the original model to commonly inseparable datasets. By simplifying the model structure, the proposed method further improves the classification accuracy and the generalization ability. Experimental results show that the proposed method achieves better performance compared with some other piecewise linear classifiers and its effectiveness and advantages are verified.
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