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A Comparison of Composed Objective Rule Evaluation Indices Using PCA and Single Indices

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

Abstract

In order to support rule evaluation procedure of human experts, objective rule evaluation indices, such as accuracy, coverage, support and other interestingness measures have been developed. However, the relationship between their values and real human evaluations has not been clarified. In this paper, we compared the availability of sorting of composed objective rule evaluation indices to that of each index. To compose objective rule evaluation indices, we used Principle Component Analysis to a dataset of their values for rule sets from 32 UCI common datasets. By using a rule set with the real human evaluation for the meningitis dataset, we performed a comparison of a sorting availability to determine the human evaluations between the composed objective rule evaluation indices and each single index. The result shows that the composed indices perform equally by comparing to the best single indices based on the sorting avalibality.

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Abe, H., Tsumoto, S. (2009). A Comparison of Composed Objective Rule Evaluation Indices Using PCA and Single Indices. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_20

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  • DOI: https://doi.org/10.1007/978-3-642-02962-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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