An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data

Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show t...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Susmita Datta, Vasyl Pihur, Somnath Datta
• Keywords: Algorithm, BMCBI 2010, Performance Measures, Performances