Fuzzy SVM for noisy data: A robust membership calculation method
Gyeongyong Heo, Paul Gader · 2009
Support vector machine (SVM) is a theoretically well motivated algorithm developed from statistical learning theory, that have shown good performance in many fields. In spite of its success, it still suffers from a noise sensitivity problem. To relax this problem, the SVM was extended by the introduction of fuzzy memberships to the fuzzy SVM (FSVM). The FSVM also has been extended further in two ways: by adopting a different objective function with the help of domain-specific knowledge and by employing a different membership calculation method. In this paper, we propose a new membership calculation method, that belongs to the second group. It is different from previous ones in that it does not assume any simple data distribution and does not need any prior knowledge. The proposed method is based on reconstruction error, which measures the agreement between the overall data structure and a data point. Thus the reconstruction error can represent the degree of outlier-ness and help in achieving noise robustness. Experimental results with synthetic and real data sets also support this.