抽象的な

Novel Way of Medical Datasets Classification Using Evolutionary Functional Link Neural Network

Sahu A and Pattnaik S

Computational time is high for Multilayer perceptron (MLP) trained with backpropagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm optimization (PSO) is used as learning tool for solving the problem of classification in data mining. EFLANN overcomes the non-linearity nature of problems by using the functionally expanded selected features, which is commonly encountered in single layer neural networks. The model is empirically compared to FLANN gradient descent learning algorithm, MLP and radial basis function (RBF). The results proved that the proposed model outperforms the other models on medical datasets classification.