Improving Multiclass ILP by Combining Partial Rules with Winnow Algorithm: Results on Classification of Dopamine Antagonist Molecules

In this paper, we propose an approach which can improve Inductive Logic Programming in multiclass problems. This approach is based on the idea that if a whole rule cannot be applied to an example, some partial matches of the rule can be useful. The most suitable class should be the class whose important partial matches cover the example more than those from other classes. Hence, the partial matches of the rule, called partial rules, are first extracted from the original rules. Then, we utilize the idea of Winnow algorithm to weigh each partial rule. Finally, the partial rules and the weights are combined and used to classify new examples. The weights of partial rules show another aspect of the knowledge which can be discovered from the data set. In the experiments, we apply our approach to a multiclass real-world problem, classification of dopamine antagonist molecules. The experimental results show that the proposed method gives the improvement over the original rules and yields 88.58% accuracy by running 10-fold cross validation.