Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Prescient Precision Utilizing GABASS Approach over Bank Data

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Kanika Choudhary, Jaykant Pratap Singh Yadav, Pradeep Kumar
10.5120/ijca2017914987

Kanika Choudhary, Jaykant Pratap Singh Yadav and Pradeep Kumar. Prescient Precision Utilizing GABASS Approach over Bank Data. International Journal of Computer Applications 171(2):27-30, August 2017. BibTeX

@article{10.5120/ijca2017914987,
	author = {Kanika Choudhary and Jaykant Pratap Singh Yadav and Pradeep Kumar},
	title = {Prescient Precision Utilizing GABASS Approach over Bank Data},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {171},
	number = {2},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {27-30},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume171/number2/28155-2017914987},
	doi = {10.5120/ijca2017914987},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

For improving accuracy in present work experiment is proposed over bank data to classify, according to the 11 existing feature. Classification problems frequently have a large number of features, but not all of them are utile for classification. Redundant and irrelevant features may be reduced the classification accuracy. Feature selection is a procedure of choosing a subset of significant components, which can diminish the dimensionality, abbreviate the running time. Genetic algorithm as an optimization tool and Naïve Bayes classifier will be used to compute the accuracy.

References

  1. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Simon Fraser University, 2000
  2. Ron Kohavi, George H. John, “Wrappers for feature subset selection”, Artificial Intelligence 97, pp. 273-324, 1996.
  3. Syed Imran Ali, Waseem Shahzad, “A Feature Subset Selection Method based on Symmetric Uncertainty and Ant Colony Optimization”, International Journal of Computer Applications (0975 – 8887) Volume 60– No.11, 2012
  4. Rajdev Tiwari, Manu Pratap Singh, “Correlation-based Attribute Selection using Genetic Algorithm” International Journal of Computer Applications, pp. 0975 – 8887 Volume 4– No.8, 2010
  5. V. Bolon-Canedo, N. Sanchez-Marono, A. Alonso-Betanzos “Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset” Elsevier Expert Systems with Applications 38, 5947–5957, 2011
  6. Asha Gowda Karegowda, M.A.Jayaram, A.S .Manjunath “Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning” International Journal of Computer Applications (0975 – 8887) Volume 23– No.2, 2011
  7. Divya Chaudhary “Data Mining: Techniques and Algorithms” International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 8, , pp. 475-479 August 2013
  8. T.R. Jerald Beno, M. Karnan, “ Dimensionality Reduction: Rough Set Based Feature Reduction” International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012
  9. R. Venkata Rao, Vivek Patel, “An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems”, International Journal of Industrial Engineering Computations 3, pp. 535–560, 2012
  10. Kashif Javed Butt, “A study of feature selection algorithms for accuracy estimation” Master in Artificial Intelligence, 2012

Keywords

Data mining, Feature selection subset, Data set, GABASS, Naïve Bayes classifier.