Fuzzy Passive–Aggressive classification: A robust and efficient algorithm for online classification problems

Fuzzy weighting, which is designed to reduce the effects of outliers for batch classification problems, might generate unreasonable membership grades especially for the samples following an input outlier, when incorporated into online classification algorithms directly. In this paper, a generalized framework for online fuzzy weighting is presented, which incrementally calculates the membership of each incoming sample by taking into account the membership grades of previous samples in a pairwise manner. The advocated pairwise-distance based scheme can not only identify possible outliers, but also show good adaptation to the sequentially received samples in the online setting. We apply it to online Passive–Aggressive (PA) algorithm in a direct way. The resulting Fuzzy Passive–Aggressive (FPA) algorithm achieves comparable classification accuracy with benchmark incremental SVM, while still enjoying the time efficiency of simple PA, which is a Perceptron-like algorithm. Besides, FPA exhibits the best performance among PA family, which makes it a robust and efficient alternative to PA, in order to deal with unavoidable outliers in large-scale or high-dimensional real datasets. The study is supported by a series of experiments with IDA benchmark repository, as well as two real-world problems namely place recognition and radar emitter recognition.