  Supervised classification is the most active and emerging research trends in
today's scenario. In this view, Artificial Neural Network (ANN) techniques have
been widely employed and growing interest to the researchers day by day. ANN
training 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 which
consists of many local optima in the search landscape. In this paper, an
enhanced Particle Swarm Optimization is proposed to minimize the error function
for classifying real-life data sets. A stability analysis is performed to
establish the efficiency of the proposed method for improving classification
accuracy. The performance measurement such as confusion matrix, $F$-measure and
convergence graph indicates the significant improvement in the classification
accuracy.
