Feature Selection Using Evolutionary Functional Link Neural Network for Classification

Abstract

<p>Computational time is high for Multilayer perceptron (MLP) trained with back propagation 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 MLP, FLANN gradient descent learning algorithm, Radial Basis Function (RBF) and Hybrid Functional Link Neural Network (HFLANN) . The results proved that the proposed model outperforms the other models.</p>

Classification of mammograms based on features extraction techniques using support vector machine

“…In this classification, positive/negative indicates the decision which has been made by the algorithm, and true/false indicates the way by which the decision agrees with the actual clinical state. Where in case the two classes there are only four possible outputs represented elements of the confusion matrix of (2 * 2) for a binary classifier see Figure 9 [29], [30]. Figure 9.…”