Int J Neural Syst - Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning.

Tópicos

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Resumo

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.

Resumo Limpo

current method combin classif focus intens research proper design group combin classifi exploit knowledg gather pool elementari classifi can success outperform singl classifi two essenti issu consid creat combin classifi establish comprehens pool design fusion model allow take full advantag collect knowledg work address issu propos adass train algorithm dedic compound classifi system effect exploit local special elementari classifi effect train procedur consist two phase first phase detect classifi compet adjust respect fusion paramet second phase boost classif accuraci elev degre local special qualiti propos algorithm evalu basi wide rang comput experi show adass can outperform origin method sever refer classifi

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