Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers

Abstract

Ensemble classifiers are very useful tools which can be applied for classification and prediction tasks in many real-world applications. There are many popular ensemble classifier generation techniques including neural network based techniques. However, there are many problems with ensemble classifiers when we apply them to real-world data of different size. This paper presents and investigates an approach for finding the impact of various parameters such as attributes, instances, classes on clusters, accuracy and diversity. The primary aim of this research is to see whether there is any link between these parameters and accuracy and diversity. The secondary aim is to see whether we can find any relationship between number of clusters in ensemble classifier and data variables. A series of experiments has been conducted by using different size of UCI machine learning benchmark datasets and neural network ensemble classifiers.