This Book Offers A Coherent And Comprehensive Approach To Feature Subset Selection In The Scope Of Classification Problems, Explaining The Foundations, Real Application Problems And The Challenges Of Feature Selection For High-dimensional Data.the Authors First Focus On The Analysis And Synthesis Of Feature Selection Algorithms, Presenting A Comprehensive Review Of Basic Concepts And Experimental Results Of The Most Well-known Algorithms. They Then Address Different Real Scenarios With High-dimensional Data, Showing The Use Of Feature Selection Algorithms In Different Contexts With Different Requirements And Information: Microarray Data, Intrusion Detection, Tear Film Lipid Layer Classification And Cost-based Features. The Book Then Delves Into The Scenario Of Big Dimension, Paying Attention To Important Problems Under High-dimensional Spaces, Such As Scalability, Distributed Processing And Real-time Processing, Scenarios That Open Up New And Interesting Challenges For Researchers.the Book Is Useful For Practitioners, Researchers And Graduate Students In The Areas Of Machine Learning And Data Mining.