Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/54382
Title: Supervised feature selection based on rough set theory and expectation-maximization algorithm
Authors: Zhang, Dong
Keywords: DRNTU::Engineering
Issue Date: 2013
Abstract: This report studies the feature selection based on the Expectation-Maximization Rough Set (RSEM) algorithm. The Expectation-Maximization clustering method extends the classical Rough Set concept of equivalent classes to tolerance classes, and enables the Feature Selection methods based on the traditional Rough Set theory to effectively deal with datasets with real values. The current RSEM algorithm is reviewed by both reproducing the results in the literature and applying three new classifiers to evaluate the features selected against a new fuzzy-rough algorithm. An improvement of the RSEM algorithm is proposed by changing the feature set evaluation method. The improved algorithm produces smaller feature sets by utilizing information hidden in the boundary region, without compromising the classification accuracies.
URI: http://hdl.handle.net/10356/54382
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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