Abstract
Drug names recognition (DNR) that seeks to discriminate drug mentions within the unstructured biomedical texts and categorize them into drug classes, is an essential task in mining medical information. The neural network structure considerably influences their overall performance. If there were lots of neurons within a network, it might fit the training dataset better, and the network might achieve few training errors, however it could be prone to over-fitting and the poor network generalization. Therefore, this paper proposes a novel network model for DNR based on a hybrid genetic artificial bee colony (GABC) algorithm. The algorithm enhances exploitation process of ABC and avoids the premature convergence problem by combining genetic operators into the ABC. These operators are hybridized in two phases of ABC: (1) exploitation process of onlooker bees phase, to enhance information sharing in between employed and onlooker bees which can find better solutions, and (2) scout bees phase to improve the replacement of any exhausted solution. The proposed network model for DNR was compared to some ether established models using different bio-inspired algorithms. The results reveal that the proposed model outperforms the other models in terms of predictive ability and computational time.