원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.7 No.3
2014.05
pp.167-176
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Shape of granule is one of the important issues in granular computing classification problems and related to the classification accuracy, the number of granule, and the join process of two granules. A bottle up granular computing classification algorithm (BUGrC) is developed in the frame work of fuzzy lattices. Firstly, the granules are represented as 4 shapes, namely hyperdiamond granule, hypersphere granule, hypercube granule, and hyperbox granule. Secondly, the granule set is induced by the training set and the bottle up join operator. Thirdly, machine learning benchmark datasets are used to analyze and discuss the BUGrC with different shape granules.
목차
Abstract
1. Introduction
2. Motivation and Related Work
2.1. Motivation
2.2. Related Work
3. Bottle Up Granular Computing Classification Algorithms
3.1. Representation of Four Kinds Shapes of Granules
3.2. Join Operator for BUGrC
3.3 Algebra System Induced by Granule Set and Inclusion Relation
3.4. Bottle Up Granular Computing Classification Algorithms
4. Experiments
4.1. Classification Problems in space R2
4.2. Classification Problems in Space RN
5. Conclusions
Acknowledgements
References
1. Introduction
2. Motivation and Related Work
2.1. Motivation
2.2. Related Work
3. Bottle Up Granular Computing Classification Algorithms
3.1. Representation of Four Kinds Shapes of Granules
3.2. Join Operator for BUGrC
3.3 Algebra System Induced by Granule Set and Inclusion Relation
3.4. Bottle Up Granular Computing Classification Algorithms
4. Experiments
4.1. Classification Problems in space R2
4.2. Classification Problems in Space RN
5. Conclusions
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보