CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection

by Shweta Kharya, Sunita Soni
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 9
Year of Publication: 2016
Authors: Shweta Kharya, Sunita Soni
10.5120/ijca2016908023

Shweta Kharya, Sunita Soni . Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection. International Journal of Computer Applications. 133, 9 ( January 2016), 32-37. DOI=10.5120/ijca2016908023

@article{ 10.5120/ijca2016908023,
author = { Shweta Kharya, Sunita Soni },
title = { Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 9 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number9/23817-2016908023/ },
doi = { 10.5120/ijca2016908023 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:44.386202+05:30
%A Shweta Kharya
%A Sunita Soni
%T Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 9
%P 32-37
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes.

References
  1. Abdelghani,Bellaachia.,Erhan,Guven.2006. Predicting Breast Cancer Survivability Using Data Mining Techniques . Scientific data mining workshop in conjuction with SIAM conference on Data Mining.
  2. Chen,M., Han,J., and Yu,P. 1997. IEEE Trans. Knowledge and Data Eng.8(866) .
  3. Diana, D. 2009. Prediction of recurrent events in breast cancer using the Naive Bayesian Classification. Annals of University of Craiova, Math. Comp. Sci. 36(2):92-96 ISSN: 1223-6934.
  4. Harry, Z.,Shengli,S. 2004.Learning weighted Naive Bayes with accurate Ranking. 4th IEEE International Conference on Data Mining.567-570,ISBN-0-7695-2142-8.
  5. Item Intensities. Knowledge and Information Systems, 6(2):203–229.
  6. Kharya ,S.2012. Using data mining techniques for diagnosis and prognosis of cancer disease. International Journal of Computer Science, Engineering and Information Technology 2(2):55-66.
  7. Kharya, S., Agrawal, S., and Soni,S.2014. Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer. International Journal of Computer Applications (0975 – 8887) Volume 92 (10):26-31.
  8. Mannila, H.1996.Methods and problems in data mining..Proc. of Int. Conf. on Database Theory.
  9. Mohd,F.,Thomas,M.,2007.Comparison of different classification techniques using WEKA for Breast cancer.IFMBE proceedings 15:520-523.
  10. Perichinsky,G., and R, Garc´ıa-Mart´ınez.2000 .Proc. Workshop Comput. Sc. Researchers (La Plata University Press, Buenos Aires. 107
  11. Perichinsky,G., R, Garc´ıa-Mart´ınez., and A. Proto.2000 .Knowledge Discovery Based on Computational Taxonomy And Intelligent Data Mining, CD of the VI Comput. Sc. Argentinean Congr.
  12. Perichinsky,G., R,Garc´ıa-Mart´ınez., A, Proto., A,Sevetto., and D, Grossi.2001. Data Mining: Supervised and Non-Supervised Intelligent Knowledge Discovery, Proc. II Workshop Computes Sc. Researchers
  13. S, Aruna., Dr S.P. Rajagopalan .,and L.V. Nandakishore.2011. Knowledge based analysis of various Statistical tools in detecting breast Cancer.CCSEA. 02:37-45.
  14. Soni,S.,Vyas,O.P 2013.Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining..Journal of Information & Knowledge Management. 12(1) 1350008 (14 pages)
  15. Soni. J. Ansari. Uzma., Sharma, D., and Soni ,S.2011.Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers.IJCSE, 3(6),ISSN:0975-3397
  16. Wang W., Yang, J., and Yu, P.S. 2004. WAR: Weighted Association Rules for item intensities.
  17. Link1-Retreived from http://csc.liv.ac.uk/~frans/KDD/software/LUCS-KDD-DN/datasets/dataSet.html.
  18. Link-2 Retrieved from UCI Machine Learning Repository. [http://archive.ics.uci.edu/ml/]. Irvine, CA: University of California, Center for Machine Learning and Intelligent Systems.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Breast cancer Naive bayes classifier Domain based weight Weights Posterior probability UCI machine learning repository Prediction.

Powered by PhDFocusTM