ESRS: a case selection algorithm using extended similarity-based rough sets

A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the ESRS algorithm, based on extended similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfacto...

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Main Authors: Liqiang Geng, Hamilton, H.J.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Clustering algorithms
Computer aided software engineering
Computer science
Frequency
Machine learning algorithms
Nearest neighbor searches
Paramagnetic resonance
Rough sets
Set theory
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Hamilton, H.J.
description A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the ESRS algorithm, based on extended similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results on synthetic and real sets of cases showed that its predictive accuracy is similar to that of well-known machine learning systems on standard data sets, while it has the advantage of being applicable to any data set where a similarity function can be defined.
doi_str_mv 10.1109/ICDM.2002.1184010
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subjects Accuracy
Clustering algorithms
Computer aided software engineering
Computer science
Frequency
Machine learning algorithms
Nearest neighbor searches
Paramagnetic resonance
Rough sets
Set theory
title ESRS: a case selection algorithm using extended similarity-based rough sets
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