Heterogeneous Ensemble Learning Algorithm Based on Label Distribution Learning
WANG Yibin1,2, TIAN Wenquan1, CHENG Yusheng1,2
1.School of Computer and Information, Anqing Normal University, Anqing 246133; 2.University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133
Abstract To improve prediction accuracy, a stacking integration framework in machine learning is employed to learn label distribution through multiple classifiers, and a heterogeneous ensemble learning algorithm based on label distribution learning(HELA-LDL) is proposed. A two-layer model framework is constructed, and the sample data are combined through the first layer structure to integrate the learning results of each classifier. Finally, the fusion results are input to the second layer classifier as the original feature, and the labels are predicted to be a label distribution with confidence. Comparative experiments on specialized datasets show that HELA-LDL is superior to other algorithms in various scenes. The stability analysis further illustrates the effectiveness of HELA-LDL.
Fund:Supported by Natural Science Research Funds of Education Department of Anhui Province(No.KJ2017A352), Foundation Project of Key Laboratories of Anhui Province(No.ACAIM160102)
Corresponding Authors:
CHENG Yusheng, Ph.D., professor. His research interests include big data, rough sets and machine learning for feature selection.
About author:: WANG Yibin, master, professor. His research interests include multi-label learning, machine learning and software security;TIAN Wenquan, master student. His research interests include machine learning, big data and data statistics.
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2019, Vol. 32 
