Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
A method for improving the accuracy of data mining classification algorithms
Computers and Operations Research
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Patent classification system using a new hybrid genetic algorithm support vector machine
Applied Soft Computing
Intelligible support vector machines for diagnosis of diabetes mellitus
IEEE Transactions on Information Technology in Biomedicine
MIMO CMAC neural network classifier for solving classification problems
Applied Soft Computing
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
Adaptive directed mutation for real-coded genetic algorithms
Applied Soft Computing
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Medical diagnosis is widely viewed as binary classification problems. To reduce classification error and parametrization of an efficient classifier, this work develops a hybrid real-coded genetic algorithm (RGA) and MIMO cerebellar model articulation controller neural network (CMAC NN) classifier. The parameter settings of the MIMO CMAC NN classifier are optimized using the RGA approach. Classification problems are then solved using the MIMO CMAC NN classifier. The performance of the proposed RGA-MIMO CMAC NN classifier is evaluated using two real-world datasets, i.e. diabetes and cancer datasets. The classification errors obtained using the RGA-MIMO CMAC NN classifier are compared with those obtained using individual MIMO CMAC NN classifier and published classifiers (e.g., genetic programming-based, NN-based and GA-based classifiers) for diabetes and cancer datasets. Experimental results indicate that the classification errors obtained using the proposed RGA-MIMO CMAC NN classifier are smaller than those of some individual and hybrid published classifiers. Moreover, the proposed approach can reduce parametrization of the MIMO CMAC NN classifier. Hence, the proposed RGA-MIMO CMAC NN classifier is highly promising for use as alternative classifier for solving medical data classification problems.