A hybrid neural network system for pattern classification tasks with missing features.

Chee-Peng Lim; Jenn-Hwai Leong; Mei-Ming Kuan

doi:10.1109/tpami.2005.64

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A hybrid neural network system for pattern classification tasks with missing features.

Chee-Peng Lim·Jenn-Hwai Leong·Mei-Ming Kuan

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Abstract

A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.

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Acute Disease Algorithms Coronary Disease Decision Support Systems, Clinical Diabetes Mellitus Diagnosis, Computer-Assisted Fuzzy Logic Humans Neural Networks, Computer Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Syndrome Comparative Study Evaluation Study Journal Article Validation Study

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Chee-Peng Lim, Jenn-Hwai Leong, Mei-Ming Kuan. 2005. A hybrid neural network system for pattern classification tasks with missing features.. https://doi.org/10.1109/tpami.2005.64

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