Introducing IVSA: A new concept learning algorithm

AbstractThe iterated version space algorithm (IVSA) has been designed and implemented to learn disjunctive concepts that have multiple classes. Unlike a traditional version space algorithm, IVSA first locates the critical attribute values using a statistical approach and then generates the base hypothesis set that describes the most significant features of the target concept. With the base hypothesis, IVSA continues to learn less significant and more specific hypothesis sets until the system is satisfied with its own performance. During the process of expanding its hypothesis space, IVSA dynamically partitions the search space of potential hypotheses of the target concept into contour-shaped regions until all training instances are maximally correctly classified. Over-fitting is not a problem for IVSA because it does not generate over-fitted candidate hypotheses during the learning