Earticle

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

키워드

저자

• Hongbing Liu [ School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China ]
• Chang-An Wu [ School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605