Active ensemble learning: Application to data mining and bioinformatics

This paper describes a new set of learning procedures which have been proposed by the authors. The method combines active learning and the accuracy enhancement techniques of bagging and boosting, and may be called active ensemble learning. Any of these procedures achieves highly accurate learning by iteratively selecting (querying) a small amount of data with large information content from a data space or database. This paper describes not only the technical aspect of the method, but also the results of application to two real problems, namely, active planning of biochemical or molecular biological experiments in immunology, and customer segmentation from a large-scale body of data in the CRM (customer relationship management) field. It is demonstrated that the proposed methods can achieve greater data efficiency and prediction accuracy than conventional methods. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(11): 100–108, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10355