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A new method for constructing multilabel classification trees is provided. A test statistic for the equality of distributions of multilabel target variable is used as a splitting criterion. The proposed method separates the splitting-variable selection step and the splitting-point selection step. The proposed method is compared with some existing methods in terms of bias and power in variable selection...
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