Improving Performance With Feature Selection, Extraction, and Learning

Abstract

In order to find significant features from raw data in the face of rising data volume and complexity, this study looks at feature selection, extraction, and training methods in machine learning. The pros and cons of using deep learning models, conventional statistical techniques, and hybrid approaches for feature selection, extraction, and training are reviewed. In order to produce machine learning models that are more useful, precise, and understandable and are applicable to healthcare, finance, and autonomous systems, the research identifies prospective future directions for research and development. To be able to pave the way for future research in supervised, unsupervised machine learning techniques, and convolutional neural networks, the article provides a thorough overview of recent methods for feature extraction, training, and selection, including the distinction between machine and deep learning algorithms.