The induction of probabilistic rule sets—the Itrule algorithm
Proceedings of the sixth international workshop on Machine learning
Machine Learning
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The paper presents two different approaches to learning by negation of the negative examples. The first approach is building a single model to represent the positive examples, as the examples not covered by the model are assumed negative. The second approach is building a double model to represent separately positive examples and negative examples. These two approaches are analysed and compared by their pros and cons.