Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2014 by IJCTT Journal
Volume-17 Number-1
Year of Publication : 2014
Authors : Anahita Ghazvini , Jamilu Awwalu , Azuraliza Abu Bakar
  10.14445/22312803/IJCTT-V17P109

MLA

Anahita Ghazvini , Jamilu Awwalu , Azuraliza Abu Bakar. "Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset". International Journal of Computer Trends and Technology (IJCTT) V17(1):39-43, Nov 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
There are different techniques in conducting data mining that range from clustering, association rule mining, prediction and classification. These techniques are applied using learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN). When conducting data mining, the choice of algorithm to use is an important decision because it depends on factors such as the nature or type of data under examination, and the target outcome of the data mining activity. In this study, we compare Naïve Bayes and Multilayer Perceptron using the classification technique as a case study on the Bank Notes dataset from the University of California Irvine (UCI) from two standpoints, which are; holdout and cross validation. Result from experiments show Multilayer Perceptron outperforms Naïve Bayes in terms of accuracy from both standpoints of holdout and cross validation.

References
[1] P. P. Tanna and Y. Ghodasara, “Foundation for Frequent Pattern Mining Algorithms ’ Implementation,” Int. J. Comput. Trends Technol., vol. 4, no. 7, pp. 2159–2163, 2013.
[2] K. Arts, “A Study On Classification Of Imbalanced Data Set 1,” Int. J. Innov. Sci. Eng. Technol., vol. 1, no. 7, pp. 247–250, 2014.
[3] A. Bruna, G. M. Farinella, G. C. Guarnera, and S. Battiato, “Forgery detection and value identification of Euro banknotes.,” Sensors (Basel)., vol. 13, no. 2, pp. 2515–29, Jan. 2013.
[4] C.-Y. Yeh, W.-P. Su, and S.-J. Lee, “Employing multiple-kernel support vector machines for counterfeit banknote recognition,” Appl. Soft Comput., vol. 11, no. 1, pp. 1439–1447, Jan. 2011.
[5] L. Sakoobunthu, “Thai Banknote Recognition Using Neural Network 1055119,” Knowledge-Based Intell. Inf. Eng. Syst., vol. 2773, 2003.
[6] H. Bhavsar and A. Ganatra, “A Comparative Study of Training Algorithms for Supervised Machine Learning,” Int. J. Soft Comput. Eng., vol. 2, no. 4, 2012.
[7] S. Vijayarani and M. Muthulakshmi, “Comparative Analysis of Bayes and Lazy Classification Algorithms,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 2, no. 8, 2013.
[8] H. Jiawei and K. Micheline, Data mining: concepts and techniques, vol. 49, no. 06. Morgan Kaufmann Publishers, 2006, pp. 49–3305–49–3305.
[9] S. B. Kotsiantis, “Supervised Machine Learning : A Review of Classification Techniques,” Informatica, vol. 31, pp. 249–268, 2007.

Keywords
Holdout, Cross validation, Naïve Bayes, Multilayer Perceptron