Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library, Band 147) - Softcover

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Dr. Verónica Bolón-Canedo received her PhD in Computer Science from the University of A Coruña, where she is currently a postdoctoral researcher. Her research interests include data mining, feature selection and machine learning.  Dr. Noelia Sánchez-Maroño received her PhD in 2005 from the University of A Coruña, where she is currently a lecturer. Her research interests include agent-based modeling, machine learning and feature selection. Prof. Amparo Alonso-Betanzos received her PhD in 1988 from the University of Santiago de Compostela, she is a Chair Professor in the Dept. of Computer Science at the University of A Coruña (Spain) and coordinator of the Laboratory for Research and Development in Artificial Intelligence. Her areas of expertise are machine learning, feature selection, knowledge-based systems, and their applications to fields such as predictive maintenance in engineering or predicting gene expression in bioinformatics.

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This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance.

With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative.

The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.