Pica-A Hybrid Feature Extraction Technique Based on Principal Component Analysis and Independent Component Analysis

Abstract

Feature Extraction (EF) is considered the effective process among all the data processing steps of the classification system. In real-life applications, the reliability of a classifier is highly affected by high-dimensional irrelevant and redundant information. Hence extraction of appropriate data plays an imperative role to reduce the dimensionality and increase the performance of the classification system. Herein paper, a hybrid Principal Independent Component Analysis (PICA) technique is presented by the combination of the two most popular Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) feature extraction techniques. The authors execute the proposed PICA technique with the SGD classifier of machine learning (ML) and analyze the performance by comparing the results with existing PCA, LDA, SVD, and ICA feature extraction techniques. Furthermore, to evaluate the PICA's performance, results are compared without applying any feature extraction techniques or with existing ICA, PCA, LDA, and SVD methods. The effectiveness of the presented work is better than existing work found in the literature and is considered on an improved scale of accomplished 3.94% accuracy, 1.35% Sensitivity, 7.70% Specificity, and 5.27% precision. Moreover, decrease the 42.60% RMSE and 15% dimensionality.