Infinite AdaBoost and its Application on Fault Diagnosis for Analog Circuits

He Xun Wang; Chong Liu; Yu Qing Sun

doi:10.4028/www.scientific.net/AMR.201-203.2070

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 201-203Infinite AdaBoost and its Application on Fault...

Infinite AdaBoost and its Application on Fault Diagnosis for Analog Circuits

Abstract:

AdaBoost algorithm can achieve better performance by averaging over the predictions of some weak hypotheses. To improve the power of classification ability of AdaBoost, an infinite ensemble learning framework based on the Support Vector Machine was formulated. The framework can output an infinite AdaBoost through embedding infinite hypotheses into a new kernel of Support Vector Machine. The stump kernel embodies infinite decision stumps. At last, the algorithm was used in fault diagnosis for analog circuits. Experimental results show that infinite AdaBoost with Support Vector Machine is superior than finite AdaBoost with the same base hypothesis set. The purpose of enhancing classification accuracy of AdaBoost algorithm is achieved.

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Advanced Manufacturing Systems, ICMSE 2011

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Periodical:

Advanced Materials Research (Volumes 201-203)

Pages:

2070-2074

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.201-203.2070

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Online since:

February 2011

Authors:

He Xun Wang, Chong Liu, Yu Qing Sun

Keywords:

Adaboost Algorithm, Classification Accuracy, Hyper-Plane, Stump Kernel, Support Vector Machine (SVM)

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