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Implement ML-c4.5 #10
A. Clare, R.D. King, Knowledge discovery in multi-label phenotype data, in: Proceedings of the 5th European Conference on PKDD, 2001, pp. 42–53.
Multi-Label C4.5 (ML-C4.5 )  is an adaptation of the well known C4.5 algorithm for multi-label learning by allowing multiple labels in the leaves of the tree. Clare et al.  modified the formula for calculating entropy (see Eq. (1)) for solving multi-label problems. The modified entropy sums the entropies for each individual class label. The key property of ML-C4.5 is its computational efficiency:
where E is the set of examples, p(ci) is the relative frequency of class label c i and q(ci)=1−p(ci).