FCM_FS: A Simultaneous Clustering and Feature Selection Model for Classification
Ming Yang, Jing Song, Genlin Ji · 2009
Fuzzy relational classifier (FRC) is the recently proposed two-step nonlinear classifiers, which effectively integrates the formed clusters and the given classes. However, FRC can not copy with the influence of those irrelevant or redundant features. To effectively filter out those irrelevant features and preserve the internal structure hidden in the given data, in this paper, a simultaneous clustering and feature selection framework called FCM_FS is introduced, which incorporates margin based feature selection criterion into the unsupervised fuzzy c-means(FCM) clustering. Based on FCM_FS and FRC framework, we introduce an enhanced FRC (EFRC). The experimental results on 8 real-life benchmark datasets show that: EFRC can consistently outperform FRC in classification performance.