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Pattern Recognition with Fuzzy Objective Function Algorithms
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
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NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Information Sciences: an International Journal
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We present the Nearest Subclass Classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the Maximum Variance Cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency.