A Review on Feature Extraction Techniques using Machine Learning

Abstract

Feature extraction plays an essential role in enhancing the performance of machine learning models by identifying and selecting appropriate information from raw data. It offers a comprehensive overview of several feature extraction techniques employed across various domains, including image processing, natural language processing, and sensor data analysis. Methods such as principal component analysis (PCA), k-means clustering, Generative Adversarial Network (GAN), Conditional Generative Adversarial Network (CGAN), Auto encoder, Bag of Words (Bow), Gray-Level-Co-Occurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) are discovered in detail, highlighting their strengths and limitations. It deliberates the reputation of feature selection standards, dimensionality reduction, and the impact of feature extraction on model interpretability and simplification. Through a comparative analysis of popular feature extraction methods, this work intentions to provide researchers and practitioners with understands into selecting appropriate techniques to optimize model performance in different applications.