| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
In this study, we proposed a rule-based classification method that uses artificial missing values to improve the effectiveness and precision of data analysis. We apply artificial missing values to avoid the sharp boundary problem encountered when discretizing continuous variables. In discretization, we treat attribute values near the boundary as missing values. We evaluated the performance of the proposed artificial missing value-based classification method and our experimental results using medical data showed this method to be effective for classification. In addition, we obtained new knowledge that led to a method of applying artificial missing values to causal analysis and a method finding similar cases in a large scale data.
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