Abstract
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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Abe, H., Tsumoto, S., Ohsaki, M., Yamaguchi, T. (2007). Evaluating a Constructive Meta-learning Algorithm for a Rule Evaluation Support Method Based on Objective Indices. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_117
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DOI: https://doi.org/10.1007/978-3-540-74827-4_117
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