Combination with Machine Learning Algorithms for the Classification in E-Bussiness

Lei Shi; Xin Ming Ma; Xiao Hong Hu

doi:10.4028/www.scientific.net/AMR.230-232.625

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 230-232Combination with Machine Learning Algorithms for...

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

Abstract:

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

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Periodical:

Advanced Materials Research (Volumes 230-232)

Pages:

625-628

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.230-232.625

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Online since:

May 2011

Authors:

Lei Shi, Xin Ming Ma, Xiao Hong Hu

Keywords:

E-Bussiness, Machine Learning (ML), Rough Set (RS)

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