Combination with Machine Learning Algorithms for the Classification in E-Bussiness
| Periodical | Advanced Materials Research (Volumes 230 - 232) |
|---|---|
| Main Theme | Frontiers of Manufacturing Science and Measuring Technology |
| Edited by | Ran Chen and Wenli Yao |
| Pages | 625-628 |
| DOI | 10.4028/www.scientific.net/AMR.230-232.625 |
| Citation | Lei Shi et al., 2011, Advanced Materials Research, 230-232, 625 |
| Online since | May, 2011 |
| Authors | Lei Shi, Xin Ming Ma, Xiao Hong Hu |
| Keywords | E-Bussiness, Machine Learning, Rough Set |
| Price | US$ 28,- |
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.
