Please use this identifier to cite or link to this item: https://doi.org/10.3233/IDA-1997-1302
Title: Feature selection for classification
Authors: Dash, M. 
Liu, H. 
Keywords: Classification
Feature selection
Framework
Issue Date: 1997
Citation: Dash, M.,Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis 1 (3) : 131-156. ScholarBank@NUS Repository. https://doi.org/10.3233/IDA-1997-1302
Abstract: Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970's to the present. It identifies four steps of a typical feature selection method, and categorizes the different existing methods in terms of generation procedures and evaluation functions, and reveals hitherto unattempted combinations of generation procedures and evaluation functions. Representative methods are chosen from each category for detailed explanation and discussion via example. Benchmark datasets with different characteristics are used for comparative study. The strengths and weaknesses of different methods are explained. Guidelines for applying feature selection methods are given based on data types and domain characteristics. This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications. © 1997 Elsevier Science B. V.
Source Title: Intelligent Data Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/99289
ISSN: 1088467X
DOI: 10.3233/IDA-1997-1302
Appears in Collections:Staff Publications

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