A novel framework for multi-class classification via ternary smooth support vector machine

作者：Chang Chih Cheng; Chien Li Jen^*; Lee Yuh Jye

来源：Pattern Recognition, 2011, 44(6): 1235-1244.

DOI：10.1016/j.patcog.2010.11.016

• Confidence
• Hidden classes
• Multi-class classification
• Smooth method
• Support vector machine
• Ternary voting games

This paper extends the previous work in smooth support vector machine (SSVM) from binary to k-class classification based on a single-machine approach and call it multi-class smooth SVM (MSSVM). This study implements MSSVM for a ternary classification problem and labels it as TSSVM. For the case k > 3, this study proposes a one-vs.-one-vs.-rest (OOR) scheme that decomposes the problem into k(k - 1)/2 ternary classification subproblems based on the assumption of ternary voting games. Thus, the k-class classification problem can be solved via a series of TSSVMs. The numerical experiments in this study compare the classification accuracy for TSSVM/OOR, one-vs.-one, one-vs.-rest schemes on nine UCI datasets. Results show that TSSVM/OOR outperforms the one-vs.-one and one-vs.-rest for all datasets. This study includes further error analyses to emphasize that the prediction confidence of OOR is significantly higher than the one-vs.-one scheme. Due to the nature of OOR design, it can detect the hidden (unknown) class directly. This study includes a "leave-one-class-out" experiment on the pendigits dataset to demonstrate the detection ability of the proposed OOR method for hidden classes. Results show that OOR performs significantly better than one-vs.-one and one-vs.-rest in the hidden-class detection rate.

• 出版日期2011-6