Combining rough decisions for intelligent text mining using Dempster’s rule |
| |
Authors: | Yaxin Bi Sally McClean Terry Anderson |
| |
Affiliation: | (1) School of Computing and Mathematics, University of Ulster, Newtownabbey, Antrim, BT37 0QB, Northern Ireland, UK;(2) School of Computing and Information Engineering, University of Ulster, Coleraine, Londonderry, BT52 1SA, Northern Ireland, UK |
| |
Abstract: | An important issue in text mining is how to make use of multiple pieces knowledge discovered to improve future decisions.
In this paper, we propose a new approach to combining multiple sets of rules for text categorization using Dempster’s rule
of combination. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model
classification decisions from multiple sets of rules as pieces of evidence which can be combined by Dempster’s rule of combination.
We apply these methods to 10 of the 20-newsgroups—a benchmark data collection (Baker and McCallum 1998), individually and
in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on
the 10 groups of the benchmark data is statistically significant and better than that of the best single set of rules. The
comparative analysis between the Dempster–Shafer and the majority voting (MV) methods along with an overfitting study confirm
the advantage and the robustness of our approach. |
| |
Keywords: | Rule induction Text mining Rough set Dempster’ s rule of combination |
本文献已被 SpringerLink 等数据库收录! |
|