Develop Local Fuzzy Classifier to Modify Low-Confidence Output of Global Classifier

Ping Ling; Xiang Sheng Rong; Yong Quan Dong; Guo Sheng Hao

doi:10.4028/www.scientific.net/AMR.989-994.1762

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Develop Local Fuzzy Classifier to Modify...

Develop Local Fuzzy Classifier to Modify Low-Confidence Output of Global Classifier

Abstract:

This paper proposes an assembling classifier consisting of a global classifier and a local classifier, named as GCLC. To this end, we present a weighted Support Vector Machine (wSVM) that serves as the global classifier, and a fuzzy k-nearest neighbor (fkNN) that serves as the local one. When a query arrives, wSVM labels it firstly. If the global decision is below some threshold, the local fkNN works to provide an improved decision. Extensive experiments on real datasets demonstrate the performance of GCLC compared with the state of the art.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1762-1765

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.1762

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Online since:

July 2014

Authors:

Ping Ling*, Xiang Sheng Rong, Yong Quan Dong, Guo Sheng Hao

Keywords:

Global Classifier, k-Nearest Neighbor Classifier, Local Fuzzy Classifier, Support Vector Machine (SVM)

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