Design of a weighted meta classifier for imbalance data having heterogeneous features
Irani Hazarika, Rupam Chodhury, Anjana Kakoti Mahanta · 2016
This paper deals with the design of a weighted ensemble of classifiers to classify imbalance data having heterogeneous features. For this purpose, a meta ensemble model is created and instead of class labels, the output of each base classifier used in the ensemble model is transformed into a [class label, weight] pair to deal with the problem. The performances of the proposed method on various datasets are calculated using the measures - Classification accuracy and G-means. The results obtained from the proposed method are compared with other methods and the proposed one shows good results in most of the cases.