An ART-Based Hybrid Network for Medical Pattern Classification Tasks with Missing Data
In this paper, a hybrid neural network capable of incremental learning and classification of patterns with missing features is proposed. Fuzzy ARTMAP (FAM) is employed as the constituting network for pattern classification while a number of Fuzzy c-Means (FCM) clustering algorithms are used for undertaking the incomplete data problem. To handle training samples with missing features, FAM is first trained with complete samples. Training samples containing missing features are then presented, and the missing values are estimated and replaced by using two FCM-based algorithms. After that, network training is conducted again with all the complete and estimated samples. To handle test samples with missing features, a non-substitution FCM-based approach is employed to yield a predicted output rapidly. A real medical database is used to evaluate, empirically, the performance of the hybrid FAM-FCM network. The results are analysed, discussed, and quantified statistically with the bootstrap method.
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