A Novel Approach of Rough Set-Based Attribute Reduction Using Fuzzy Discernibility Matrix
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Data attribute reduction using binary conversion
WSEAS Transactions on Computers
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
Consistency based attribute reduction
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
A heuristic algorithm based on attribute importance for feature selection
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
A novel approach to attribute reduction in formal concept lattices
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
| Hi-index | 0.00 |
Rough set is a widespread concept in computer science and is applicable in many fields such as artificial intelligence, expert systems, data mining, pattern recognition and decision support systems. One of key problems of knowledge acquisition in theoretical study of rough sets is attribute reduction. Attribute reduction also called feature selection eliminates superfluous attributes in the information system and improves efficiency of data analysis process. But reducing attributes is a NP-hard problem. Recently, to overcome the technical difficulty, there are a lot of research on new approaches such as maximal tolerance classification (Fang Yang et al. 2010), genetic algorithm (N. Ravi Shankar et al. 2010), topology and measure of significance of attributes (P.G. JansiRani and R. Bhaskaran 2010), soft set (Tutut Herawan et al. 2010), positive approximation (Yuhua Qian et al. 2010), dynamic programming (Walid Moudani et al. 2010). However, there are still some challenging research issues that time consumption is still hard problem in attribute reduction. This paper introduces a new approach with a model presented with definitions, theorems, operations. Set of maximal random prior forms is put forward as an effective way for attribute reduction. The algorithm for seeking maximal random prior set are proposed with linear complexity, contributes to solve absolutely problems in attribute reduction and significantly improve the speed of calculation and data analysis.