Probabilistic rule induction with the LERS data mining system
Probabilistic rule induction with the LERS data mining system
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DOI：
10.1002/int.20482
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发表时间：
2011-06
影响因子：
7
通讯作者：
J. Grzymala-Busse;Yiyu Yao
中科院分区：
文献类型：
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作者：
J. Grzymala-Busse;Yiyu Yao
LERS（Learning from Examples based on Rough Sets）数据挖掘系统基于经典的粗糙集近似，归纳出两类规则，即从下近似中得到的确定性规则和从上近似中得到的可能性规则。通过放松经典粗糙集的严格要求，可以得到概率近似。LERS可以很容易地应用于从概率正区域和边界区域导出概率正规则和边界规则。本文讨论了LERS概率规则归纳的几个基本问题，包括规则归纳算法、规则的量化度量以及规则冲突的消解方法。© 2011 Wiley Periodicals，Inc.
Based on classical rough set approximations, the LERS (Learning from Examples based on Rough Sets) data mining system induces two types of rules, namely, certain rules from lower approximations and possible rules from upper approximations. By relaxing the stringent requirement of the classical rough sets, one can obtain probabilistic approximations. The LERS can be easily applied to induce probabilistic positive and boundary rules from probabilistic positive and boundary regions. This paper discusses several fundamental issues related to probabilistic rule induction with LERS, including rule induction algorithm, quantitative measures associated with rules, and the rule conflict resolution method. © 2011 Wiley Periodicals, Inc.