Earticle

초록

영어

Data mining is the process of extracting useful information from the vast and complex databases. In real time scenario the data sources contain many varied data including imbalance data category. Imbalance data sets contain more percentage of instances from one class and are very less percentage of instances from other class. The traditional decision tree algorithm called Iterative Dichotomiser 3 (ID3) is built for not handling the imbalance datasets. To overcome the drawback of ID3 on imbalance datasets, an improved algorithms are needed. In this paper, propose extension of ID3 algorithm called Over Sampled ID3 (OSID3) for imbalance data learning. The proposed OSID3 approach uses the oversampling technique with unique statistical oversample strategy for removing less privileged instances in the early stage and later on oversampling the high privileged instances for approximate data balance. The experimental observation suggests that the proposed approach improves in terms of Accuracy, Area Under Curve (AUC) and Root Mean Square Error (RMSE) with the benchmark ID3 on 15 imbalance datasets from University of California, Irvine (UCI) repository.

목차

Abstract
 1. Introduction
 2. Current Approaches in Decision Trees
 3. The Proposed Approach
 4. Investigational Design and Assessment Criteria
 5. Results
 6. Conclusion
 References

키워드

저자

• L. Surya Prasanthi [ Research Scholar, Department of Computer Science, Krishna University, Machilipatnam, India ]
• R. Kiran Kumar [ Department of Computer Science, Krishna University, Machilipatnam, India ]
• Kudipudi Srinivas [ Department of Computer Science & Engineering, V.R. Siddartha Engineering College, Vijayawada, India ]

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