Evaluating the effect of rough set feature selection on the performance of decision trees

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Feature selection is a pre-processing step for training of classifiers in order to improve their performance. Rough Set Feature Selection (RSFS) is a novel feature selection approach. RSFS removes the redundant attributes only while keeping all the important ones that preserve the classification power of the original dataset. The feature subsets selected by RSFS are called reducts. The intersection of all reducts is called the core. This paper investigates the effect of RSFS on the performance of decision trees in terms of classification accuracy and number of tree nodes. 9 datasets from different domains are used. For all datasets, there exists at least 1 reduct improving the performance of decision trees and the minimal reduct is not the best-quality reduct in improving decision tree performance. The effect of RSFS on the performance of the decision trees is shown to be related to the ratio of core size to dataset dimensionality. The core size is shown to be determined by the presence of pairs of core-determining objects within the dataset. © 2006 IEEE.
    Original languageEnglish
    Title of host publication2006 IEEE International Conference on Granular Computing|2006 IEEE Int. Conf. Granular Comp.
    PublisherIEEE
    Pages57-62
    Number of pages5
    ISBN (Print)1424401348, 9781424401345
    Publication statusPublished - 2006
    Event2006 IEEE International Conference on Granular Computing - Atlanta, GA
    Duration: 1 Jul 2006 → …

    Conference

    Conference2006 IEEE International Conference on Granular Computing
    CityAtlanta, GA
    Period1/07/06 → …

    Keywords

    • Decision tree learning
    • Discretization
    • Genetic algorithms
    • Ratio of core size to dataset dimensionality
    • Rough set feature selection

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