Feature Selection in Decision Systems Based on Conditional Knowledge Granularity
- 10.2991/ijcis.2011.4.4.21How to use a DOI?
- Conditional knowledge granularity, Rough sets, Attribute reduction, Feature selection, Rough communication
Feature selection is an important technique for dimension reduction in machine learning and pattern recognition communities. Feature evaluation functions play essential roles in constructing feature selection algorithms. This paper introduces a new notion of knowledge granularity, called conditional knowledge granularity, reflecting relationship between conditional attributes and decision attribute. An evaluation function to measure significance of conditional attributes is proposed and equivalent characterization of attribute reduction is established based on the conditional knowledge granularity. An optimal algorithm for feature selection is developed on the basis of the proposed evaluation function. Furthermore, a novel approach to performing feature selection in an inconsistent decision system is put forward through establishing a rough communication between the inconsistent decision system and a consistent decision system. Simulated experiments verifies feasibility and efficiency of the proposed technique.
- © 2011, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Tingquan Deng AU - Chengdong Yang AU - Qinghua Hu PY - 2011 DA - 2011/06/01 TI - Feature Selection in Decision Systems Based on Conditional Knowledge Granularity JO - International Journal of Computational Intelligence Systems SP - 655 EP - 671 VL - 4 IS - 4 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2011.4.4.21 DO - 10.2991/ijcis.2011.4.4.21 ID - Deng2011 ER -