| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2013: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Outline of Final Research Achievements |
There are two goals for data mining from numerical data. One is to maximize the accuracy of obtained knowledge. The other is to maximize its interpretability. However, there is a tradeoff between the accuracy and the interpretability. To address this issue, we proposed multiobjective genetics-based machine learning (MoGBML) which can simultaneously handle these two objectives and provide a number of classifiers with different accuracy and interpretability as knowledge. To further extend MoGBML to large data sets, we apply our parallel distributed implementation to MoGBML in this study. In addition, we examine the effects of a various kind of antecedent sets on the performance of our parallel distributed GBML. We also examine the applicability of our parallel distributed implementation to data mining from multiple data centers.
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