PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine

Abstract

Principal component analysis (PCA) is one of the crucial dimensionality reduction (DR) techniques in which the original features are transformed into lower dimensional space. Though the PCA space has orthogonal principal components (PC), it does not provide a real reduction of dimensionality in terms of the original features (variables), as all features including irrelevant and redundant features are required to define a single PC. It is necessary to remove such type of features by using feature subset selection (FSS) for better generalization performance. The principal intent of this paper is to introduce a PCA-based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach that is able to handle weighted classification problem. The PF-FELM is mainly categorized into four subsystems: PCA, FSS, fuzzification, and classification. FSS selects an optimal feature order that is based on maximum occurrences within the various filter-based ranking algorithms. The simulation results are computed and compared by using sequential forward search strategy for clinical datasets. As a case study, an uncertain real-time application of plant disease and nutrient deficiency classification is considered, helping remote farmers as expert advice. For validation of the proposed PF-FELM, statistical and hypothetical tests are evaluated by using correlation measure and paired t-test. With the results, it is inferred that PF-FELM provides 12.17% improved generalization performance as compared to P-FELM (without FSS) by reducing 13.94% features.