Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation

Due to the implicit characteristics of learning disabilities (LD), the identification and diagnosis of students with learning disabilities has long been a difficult issue. Identification of LD usually requires extensive man power and takes a long time. Our previous studies using smaller size samples have shown that ANN classifier can be a good predictor to the identification of students with learning disabilities. In this study, we focus on the genetic algorithm based feature selection method and evolutionary computation based parameters optimization algorithm on a larger size data set to explore the potential limit that ANN can achieve in this specific classification problem. In addition, a different procedure in combining evolutionary algorithms and neural networks is proposed. We use the back propagation method to train the neural networks and derive a good combination of parameters. Evolutionary algorithm is then used to search around the neighborhood of the previously acquired parameters to further improve the classification accuracy. The outcomes show that the procedure is effective, and with appropriate combinations of features and parameters setting, the accuracy of the ANN classifier can reach the 88.2% mark, the best we have achieved so far. The result again indicates that AI techniques do have their roles on the identification of students with learning disabilities.