Earticle

초록

영어

Prediction plays a significant role in the human life to predict the situation, climate, finance, outcome of the particular event or activities, etc. This predication can be achieved by the classifier which is formally known as supervised learner. The classifier can be built using the dataset and its performance is based on the attributes or features present in the dataset which are highly relevant to the predictive target attributes. The feature selection process removes the redundant and irrelevant features from the dataset to improve the performance of the classifier. This paper proposes a rough set-based feature selection method to remove the redundant and irrelevant features in order to improve the performance the classifier. The proposed method is tested on the various datasets with the various supervised learning algorithms and it is evident that the proposed method producing the better performance than the other methods compared.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Method
  3.1. Description of the Algorithm:
  3.2. Implementation
  3.3. Dataset Used
 4. Results and Discussion
 5. Conclusion and Future Work
 References

키워드

저자

• D. Asir Antony Gnana Singh [ University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tiruchirappalli- 620 024 ]
• E. Jebamalar Leavline [ University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tiruchirappalli- 620 024 ]
• E. Priyanka [ University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tiruchirappalli- 620 024 ]
• C. Sumathi [ University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tiruchirappalli- 620 024 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605