Optimizing Feature Selection for Big Data and Machine Learning: Challenges and Future Prospects

Feature selection is an important step in the machine learning process that improves model accuracy by filtering out unnecessary features from higher-dimensional data, eliminating redundancy, and increasing computational speed. It deletes features that are irrelevant or redundant, hence reduces the amount of time taken to train a model, and increases its chances of accuracy. Filter techniques, wrappers, embed, hybrid techniques are among the most commonly used approaches where each of them comes with its unique time and accuracy spaces. Current trends are especially concerned with tackling issues related to big data, deep learning and time series while new upcoming application areas include real-time system, edge AI. Feature selection is widely used in different fields such as health care, finance, NLP, CV, and cybersecurity. Nevertheless, some issues still open concern with regard to extendibility to an extensive amount of data, interpretability of the models, and incorporation of unsupervised learning. Subsequent studies will be devoted to surmounting these difficulties and creating more accurate, explainable, and extensible methodologies for emergent machine learning needs.