Earticle

초록

영어

The existing classifiers for uncertain data don’t consider the dynamic cost, so this paper proposes the classification approach of the dynamic cost-sensitive decision tree for uncertain data based on the genetic algorithm (GDCDTU) , which overcomes the limitations of the stationary cost, and searches automatically the suitable cost space of every sub datasets. Firstly, this paper gives the dynamic cost- sensitive learning thought, and disposes the continuous and discrete attributes for uncertain data by the probabilistic cardinality. Secondly, we give the selection methods for the splitting attributes and the construction process for cost-sensitive decision tree, and the interval number for describing dynamic cost is coded by its centre and radius. At last, the dynamic cost-sensitive decision tree for uncertain data is structured, which uses the genetic algorithm as the optimal misclassification cost searching way, and the optimum cost is got by the hybridization, the mutation, the selection. The experiments using both artificial and real data sets show that, compared to the other decision tree classification algorithms for uncertain data, GDCDTU has higher classification accuracy and performance, and the total expenditure is lower.

목차

Abstract
 1. Introduction
 2. Dynamic Cost-sensitive Learning
 3. Description for Uncertain Data
  3.1 Uncertain Discrete Attribute
  3.2. Uncertain Continuous Attribute
 4. Dynamic Cost-sensitive Decision Tree for Mining Uncertain Data Based on the Genetic Algorithm
  4.1. Chromosome Coding
  4.2. Selection of Splitting Attribute
  4.3. Structure of Cost-sensitive Decision Tree for Uncertain Data
  4.4. Fitness Function
  4.5 Classification Algorithm of Cost-sensitive Decision Tree for Uncertain Data
 5. Simulation Experiment
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Yuwen Huang [ Department of Computer and Information Engineering, Heze University, Heze 274015, Shandong, China , Key Laboratory of computer Information Processing, Heze University, Heze 274015, Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605