Journal of Systems Engineering and Electronics ›› 2009, Vol. 20 ›› Issue (6): 1344-1350.
• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles Next Articles
Chang Tiantian, Liu Hongwei & Zhou Shuisheng
Online:
Published:
Abstract:
Local diversity AdaBoost support vector machine (LDAB-SVM) is proposed for large scale dataset classification problems. The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built. In order to obtain a better performance, AdaBoost is used in each model building. In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting. Then the local models via voting method are integrated. The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.
Chang Tiantian, Liu Hongwei & Zhou Shuisheng. Large scale classification with local diversity AdaBoost SVM algorithm[J]. Journal of Systems Engineering and Electronics, 2009, 20(6): 1344-1350.
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https://www.jseepub.com/EN/Y2009/V20/I6/1344