Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/52990
Title: Incremental Feature Selection
Authors: Liu, H. 
Setiono, R. 
Keywords: Dimensionality reduction
Feature selection
Machine learning
Pattern recognition
Issue Date: 1998
Source: Liu, H.,Setiono, R. (1998). Incremental Feature Selection. Applied Intelligence 9 (3) : 217-230. ScholarBank@NUS Repository.
Abstract: Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.
Source Title: Applied Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/52990
ISSN: 0924669X
Appears in Collections:Staff Publications

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