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Abstract: In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hybrid intelligent system is proposed for medical data classification tasks. [•] The proposed system is able to learn incrementally and explain its predictions. [•] Benchmark medical data sets are used to evaluate the effectiveness of the system. [•] The results ascertain the usefulness of the system for medical decision support. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

Highlights: [•] Perform offline as well as online fault detection and diagnosis of induction motors. [•] Extract current harmonic patterns with the Motor Current Signature Analysis method. [•] Deploy the Fuzzy Min-Max (FMM) neural network to learn fault patterns incrementally. [•] Use the Classification and Regression Tree (CART) to refine the knowledge from FMM. [•] Monitor multiple motors and provide predicted fault conditions to users (engineers). [ABSTRACT FROM AUTHOR]