@conference {18449,
	title = {Ensemble LSDD-based Change Detection Tests},
	booktitle = {IEEE-INNS  International Joint Conference on Neural Networks (IJCNN16)},
	year = {2016},
	month = {07/2016},
	address = {Vancouver, Canada},
	abstract = {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.},
	author = {Bu, Li and Alippi, Cesare and Zhao, Dongbin}
}
