A Review of Feature Selection Methods in Big Data
Keywords:
data dimensionality reduction, evolutionary algorithms, feature selection, filter methods, high dimensional data, machine learning.Abstract
The exponential growth of available data has led to the emergence of high dimensional datasets characterized by hundreds of variables. Consequently, feature selection has emerged as a critical task in data mining and machine learning. Its primary objective is to mitigate the curse of dimensionality by reducing data dimensionality and enhancing algorithm performance, particularly in classification tasks. This paper aims to provide the reader with a comprehensive understanding of feature selection techniques beyond the traditional categorization into filter, wrapper, and embedded approaches. Instead, we present feature selection as a combinatorial optimization or search problem, leading to a novel categorization of methods into exhaustive search, heuristic search, metaheuristic search, embedded methods, and hybrid approaches. By examining the strengths and weaknesses of these methods, we shed light on their strengths and weaknesses. Furthermore, we address current problems and challenges in the field, paving the way for future research and identifying promising areas of investigation.
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