Towards Scalable Prototype Selection by Genetic Algorithms with Fast Criteria

How to select the prototypes for classification in the dissimilarity space remains an open and interesting problem. Especially, achieving scalability of the methods is desirable due to enormous amounts of information arising in many fields. In this paper we pose the question: are genetic algorithms good for scalable prototype selection? We propose two methods based on genetic algorithms, one supervised and the other unsupervised, whose analyses provide an answer to the question. Results on dissimilarity datasets show the effectiveness of the proposals.

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