OPTIMIZED FUZZY MIN-MAX NEURAL NETWORK: AN EFFICIENT APPROACH FOR SUPERVISED OUTLIER DETECTION

Abstract

Fuzzy min-max neural network (FMN) proposed by Simpson [18] is a well-known supervised fuzzy-neural classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in ‘fine-grained’ manner that reduce its performance for pattern recognition in terms of recall time per pattern. In this paper, we adapt the basic architecture of FMN to represent the learned knowledge in a compact way, i.e., in a ‘coarse-grained’ manner which is closed to human thinking. Working of the proposed method, that is fuzzy min-max neural network with knowledge compaction (FMN_KC), is described using Fisher Iris dataset. The potential of using FMN_KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup99 dataset available in UCI repository. The proposed method has achieved around 50% gain in the recall time compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture called FMN_KC for supervised outlier detection in real time applications where recall time per pattern is one of the key parameters.