Exploring Feature Partitioning Methods for Data Mining Applications

Abstract

Feature partitioning is a fundamental concept in machine learning and data mining, offering a crucial framework for data representation, classification, and predictive modeling. This chapter delves into the multifaceted domain of feature partitioning, exploring the methodologies, techniques, and applications that drive this field. Feature partitioning methods range from random-based approaches to pattern-based, clustering-based, performance-based, and optimization-based techniques. The chapter provides a comprehensive overview of these methods, discussing their strengths, weaknesses, and suitability for various tasks. Furthermore, it analyzes the comparative performance of these methods, emphasizing their impact on classification accuracy. In addition to this evaluation, the chapter highlights the associated issues, challenges, and opportunities in the domain of multiview ensemble learning, offering a broader perspective on its future development. As a versatile concept with applications in diverse fields, feature partitioning has a crucial function in enhancing the quality and interpretability of machine learning models. This chapter serves as an excellent source for scholars, practitioners, and students seeking a deeper understanding of feature partitioning and its significance in modern data mining applications.