Knowledge learning and understanding from incomplete data based on pattern similarity

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To acquire understandable knowledge through machine learning, we have proposed the nearest neighbor classification tree (NNC-Tree) and an induction method. NNC-Tree is a kind of multivariate decision trees based on pattern similarity. In this project, we have proposed (1) a method for selecting important features through learning ; (2) a method for unifying the learning algorithms through data fuzzification ; and (3) a method for efficient dimensionality reduction. With these new contributions, we can induce multivariate decision trees more efficiently.