Fuzzy LP-SVMs for Multiclass Problems

Abstract. In this paper, we propose fuzzy linear programming support vector machines (LP-SVMs) that resolve unclassifiable regions for multiclass problems. Namely, in the directions orthogonal to the decision functions obtained by training the LP-SVM, we define membership functions. Then by the minimum or average operation for these membership functions we define a membership function for each class. We evaluate one-against-all and pairwise fuzzy LP-SVMs for some benchmark data sets and demonstrate the superiority of our fuzzy LP-SVMs over conventional LP-SVMs.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Shigeo Abe
• Keywords: ESANN 2004, ESANN 2007, Fuzzy Linear Programming, Fuzzy Lp-svms, Membership Function