Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification

This paper presents an improved opposition-based Particle Swarm Optimizer (PSO) with partial opposition-based learning. The partial opposition-based learning scheme is a new form of opposition-based learning and it is employed to improve the performance. Nowadays, the artificial neural network (ANN), an important machine learning tool, is used in medicine especially in medical disease diagnosis. ANN training is a complex task and a training algorithm has a significant role in ANN’s performance. Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network (FFNN) for classification task in medical data mining. The classification task is carried out on 15 well-known medical data sets. A comparative study has been made with Levenberg–Marquardt algorithm, Artificial Bee Colony, Trigonometric Mutation Differential Evolution, basic PSO and other nine opposition-based PSO algorithms. The experimental results with statistical analysis demonstrate that the proposed algorithm performs better than all other algorithms in classification.