An unbiased method for constructing multilabel classification trees

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. A simulated data set and two real data sets are used to compare the accuracies of constructed trees. According to the comparative study, the proposed method outperforms existing methods in some properties.

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