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Supervised classification is the most active and emerging research trends intoday's scenario. In this view, Artificial Neural Network (ANN) techniques havebeen widely employed and growing interest to the researchers day by day. ANNtraining aims to find the proper setting of parameters such as weights($\textbf{W}$) and biases ($b$) to properly classify the given data samples.The training process is formulated in an error minimization problem whichconsists of many local optima in the search landscape. In this paper, anenhanced Particle Swarm Optimization is proposed to minimize the error functionfor classifying real-life data sets. A stability analysis is performed toestablish the efficiency of the proposed method for improving classificationaccuracy. The performance measurement such as confusion matrix, $F$-measure andconvergence graph indicates the significant improvement in the classificationaccuracy.

Accuracy Improvement of Neural Network Training using Particle Swarm Optimization and its Stability Analysis for Classification