Abstract
In this paper we introduce the supervised classification algorithm called Box algorithm based on feature space partitioning. The construction of Box algorithm is closely linked to the solution of computational geometry problem involving heuristic maximal clique cover problem satisfying the k-nearest neighbor rule. We first apply a heuristic algorithm to partition a graph into a minimal number of maximal cliques and subsequently the cliques are merged by means of the k-nearest neighbor rule. The main advantage of the new approach is decomposition of the l-class problem (\(l > 2\)) into l single-class optimization problems. The performance of the Box algorithm is demonstrated to be significantly better than SVM in computer experiments involving real Monk’s dataset from UCI depository and simulated normal data.
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