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Education and Innovation in Embedded Systems Design

USI Università della Svizzera italiana, USI Faculty of Informatics, Advanced Learning and Research Institute USI Università della Svizzera italiana USI Faculty of Informatics USI Advanced Learning and Research Institute
TitleEnsemble LSDD-based Change Detection Tests
Publication TypeConference Paper
Year of Publication2016
AuthorsBu, L., C. Alippi, and D. Zhao
Conference NameIEEE-INNS International Joint Conference on Neural Networks (IJCNN16)
Date Published07/2016
Conference LocationVancouver, Canada

The least squares density difference change detection test (LSDD-CDT) has proven to be an effective method in detecting concept drift by inspecting features derived from the discrepancy between two probability density functions (pdfs). The first pdf is associated with the concept drift free case, the second to the possible post change one. Interestingly, the method permits to control the ratio of false positives. This paper introduces and investigates the performance of a family of LSDD methods constructed by exploring different ensemble options applied to the basic CDT procedure. Experiments show that most of proposed methods are characterized by improved performance in change detection once compared with the direct ensemble-free counterpart.