A novel relative minimum distance is introduced that allows improving the dissimilarity-based multiple instance classification. To this end, we apply a previously proposed mapping that brings closer, at least, a single instance from each positive training bag, while the negative-bags instances are driven apart. Our results show an increased classification performance on a broad type of real-world datasets.
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