Feature Selection in Decision Systems Based on Conditional Knowledge Granularity

DOI

10.2991/ijcis.2011.4.4.21How to use a DOI?

Keywords

Conditional knowledge granularity, Rough sets, Attribute reduction, Feature selection, Rough communication

Abstract

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.

Copyright

© 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/).

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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  -