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Title:
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EVALUATING A LEARNING ALGORITHM SELECTIONMETHOD FOR A RULE EVALUATION SUPPORT TOOL BASED ON OBJECTIVE INDICES |
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Author(s):
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Hidenao Abe , Shusaku Tsumoto , Miho Ohsaki , Takahira Yamaguchi |
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ISBN:
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978-972-8924-40-9 |
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Editors:
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Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Data Mining, Post-processing, Rule Evaluation Support, Objective Rule Evaluation Index |
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Type:
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Full Paper |
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First Page:
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25 |
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Last Page:
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32 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this paper, we present an evaluation of learning algorithms to select proper ones in a rule evaluation support tool for
post-processing of mined results. Post-processing of mined results is one of the key processes in the data mining process.
However, it is difficult for human experts to completely evaluate several thousand of rules from a large dataset with
noises. To reduce the costs in such a rule evaluation task, we have developed a rule evaluation support method with rule
evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert
for each rule. To enhance the adaptability of rule evaluation models, we introduced a constructive meta-learning system
to choose proper learning algorithms. Then, we performed the case study on the meningitis data mining as an actual
problem. The obtained results demonstrate the applicability of the proposed rule evaluation support method. |
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