Hybrid ensemble approach for classification

This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.