Computational Methods of Feature Selection (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Huan Liu, Hiroshi Motoda, Wei Tang, Emanuele Olivetti, Susana Eyheramendy, George Forman, Eugene Tuv, Masoud Makrehchi, Carlotta Domeniconi, Yijun Sun, Rezarta Islamaj Dogan, Isabelle Guyon, Lei Yu, Jennifer G. Dy, Diana Chan, Claudia Diamantini, Joshua Z. Huang, Marko Robnik-Sikonja, Igor Kononenko, David Stracuzzi, Roberto Ruiz, David Madigan, Michael Ng, Shi Zhong, Hui Zou, Mohamed S. Kamel, Xu Jun, Lise Getoor, Trevor Hastie, Constantin Aliferis, Kari Torkkola, Susan Bridges, Paolo Avesani, Yunming Ye, Domenico Potena, Alexander Borisov, Jesus Aguilar-Ruiz, Sriharsha Veeramachaneni, Jose C. Riquelme, Shane Burgess, W. John Wilbur, Vipin Kumar

ISBN 10: 1584888784 / ISBN 13: 9781584888789

Published by Chapman and Hall/CRC 2007-10-29, 2007

About this Item

Seller Inventory # 6666-TNFPD-9781584888789

Bibliographic Details

• Title: Computational Methods of Feature Selection (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
• Publisher: Chapman and Hall/CRC 2007-10-29
• Publication Date: 2007
• Language: English
• ISBN 10: 1584888784
• ISBN 13: 9781584888789
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool.

The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection.

Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.

About the Author

Huan Liu, Hiroshi Motoda