Earticle

초록

영어

The uncertain data as an important aspect of data mining, has received considerable attention, due to its importance in many applications, but little study has been paid to the cost-sensitive classification on uncertain data, so this paper proposes the dynamic cost-sensitive Naive Bayes classification for mining uncertain data (DCSUNB). Firstly, we apply the probability density to dispose uncertain discrete and continuous attributes, and give the cost-sensitive Naive Bayes classifier. Secondly, we propose the construction process of dynamic cost, and give the evaluation method for finding the optimal cost and the cost-sensitive classification with sequential test strategy. At last, the dynamic cost-sensitive Naive Bayes algorithm for uncertain data is structured, which searches the misclassification and test cost spaces to find the optimal cost. By comparing to the other cost-sensitive classification algorithms for uncertain data, the experiments on UCI Datasets show that DCSUNB can improve the classification performance, and reduce effectively the total cost.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Probability Density of Uncertain Attribute
  2.2. Cost-sensitive Naive Bayes Classifier
 3. The Proposed Scheme
  3.1. Dynamic Test Cost
  3.2. Evaluation Method of Optimal Cost
  3.3. Cost-sensitive Classification with Sequential Test Strategy
  3.4. Dynamic Cost-Sensitive Naive Bayes Algorithm for Mining Uncertain Data
 4. Simulation Experiment
  4.1. Data Preprocessing
  4.2. Result of Experiments
 5. 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