A novel ensembling method to boost performance of neural networks
Manomita Chakraborty, Saroj Kr. Biswas, Biswajit Purkayastha · Journal of Experimental & Theoretical Artificial Intelligence · 2019
Classification is one of the important tasks in data mining. Over the last few decades, neural networks have proved to be very effective in solving this task. This paper presents a novel algorithm, called Neural Network Boosting (NNBOOST) to improve the classification performance of neural networks using ensembling technique. The ensembling technique proposed here is based on the boosting concept to ensemble classifiers. A boosting algorithm sequentially learns a new model by reassigning pattern weights. NNBOOST algorithm adapts a new technique to assign weights to patterns based on the Euclidean distances of patterns from the centroids of respective classes. It assigns more weight to patterns closest to the centroid. The pattern weights are updated subsequently after building or learning a neural network model, based on the updated centroids to build the next neural network model. Weighted Majority voting scheme is used to combine all the learned neural network models. Weight or confidence of a learned neural network model is calculated as the ratio of correctly classified to misclassified training patterns. The algorithm is validated with six real life data sets taken from UCI repository. Results show the effectiveness of the algorithm in ensembling neural networks.