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| Adaptive Classification Algorithm for Gradual Concept-Drifting Data |
| Zhang Jing-Xiang1,2,Wang Shi-Tong1,Deng Zhao-Hong1 |
1.School of Digital Media,Jiangnan University,Wuxi 214122
2.School of Science,Jiangnan University,Wuxi 214122 |
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Abstract At present,the concept-drifting phenomena in various datasets receives considerable attention. Aiming at the classification of concept drift,an adaptive neighbor projection mean discrepancy support vector machine (NMD-SVM) is proposed. The concept of the neighbor projection mean discrepancy in the reproducing kernel Hilbert space is defined to measure the discrepancy between adjacent sub-classifiers,and the distribution characteristics of data are integrated into the process of global optimization. Thus,the adaptability of classification algorithm is enhanced. The experimental results on the simulation and real datasets validate the effectiveness of the proposed algorithm.
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Received: 13 August 2012
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附录 式(5)的详细推导过程. |
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2013, Vol. 26 
