Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 45, Issue 2, Pages 177-190Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2322195
Keywords
Adaptive processes; classifier ensemble; decision tree; optimization; random subspace
Categories
Funding
- Hong Kong Scholars Program [XJ2012015]
- National Natural Science Foundation of China (NSFC) [61273363, 61379033]
- NSFC-Guangdong Joint Fund [U1035004]
- New Century Excellent Talents in University [NCET-11-0165]
- Guangdong Natural Science Funds for Distinguished Young Scholar [S2013050014677]
- Science and Technology Planning Project of Guangzhou [11A11080267]
- China Post-Doctoral Science Foundation [2013M540655]
- Foundation of Guangdong Educational Committee [2012KJCX0011]
- Fundamental Research Funds for the Central Universities [2014G0007]
- Key Enterprises and Innovation Organizations in Nanshan District in Shenzhen [KC2013ZDZJ0007A]
- Natural Science Foundation of Guangdong Province, China [S2012010009961]
- Doctoral Program of Higher Education [20110172120027]
- Cooperation Project in Industry, Education and Academy of Guangdong Province
- Ministry of Education of China [2011B090400032]
- Key Lab of Cloud Computing and Big Data in Guangzhou [SITGZ[2013]268-6]
- Hong Kong Baptist University [RGC/HKBU211212]
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Traditional random subspace-based classifier ensemble approaches (RSCE) have several limitations, such as viewing the same importance for the base classifiers trained in different subspaces, not considering how to find the optimal random subspace set. In this paper, we design a general hybrid adaptive ensemble learning framework (HAEL), and apply it to address the limitations of RSCE. As compared with RSCE, HAEL consists of two adaptive processes, i.e., base classifier competition and classifier ensemble interaction, so as to adjust the weights of the base classifiers in each ensemble and to explore the optimal random subspace set simultaneously. The experiments on the real-world datasets from the KEEL dataset repository for the classification task and the cancer gene expression profiles show that: 1) HAEL works well on both the real-world KEEL datasets and the cancer gene expression profiles and 2) it outperforms most of the state-of-the-art classifier ensemble approaches on 28 out of 36 KEEL datasets and 6 out of 6 cancer datasets.
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