A new ant colony algorithm for multi-label classification with applications in bioinfomatics

ABSTRACT

The conventional classification task of data mining can be called single-label classification, since there is a single class attribute to be predicted. This paper addresses a more challenging version of the classification task, where there are two or more class attributes to be predicted. We propose a new ant colony algorithm for the multi-label classification task. The new algorithm, called MuLAM (Multi-Label Ant-Miner) is a major extension of Ant-Miner, the first ant colony algorithm for discovering classification rules. We report results comparing the performance of MuLAM with the performance of three other classification techniques, namely the very simple majority classifier, the original Ant-Miner algorithm and C5.0, a very popular rule induction algorithm. The experiments were performed using five bioinformatics datasets, involving the prediction of several kinds of protein function.

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Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Logical and relational learning

        • Inductive logic learning