International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 8, No. 8, 8 Aug. 2016
Cover page and Table of Contents: PDF (size: 952KB)
On the Performance of Classification Techniques with Pixel Removal Applied to Digit Recognition
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Author(s)
Index Terms
Frames, trimming, Recursive Partitioning and Classification Trees, Naive Bayes, k-N.earest Neighbour, Support Vector Machine, MNIST
Abstract
The successive loss of the outermost pixel values or frames in the digital representation of handwritten digits is postulated to have an increasing impact on the degree of accuracy of categorizations of these digits. This removal of frames is referred to as trimming. The first few frames do not contain significant amounts of information and the impact on accuracy should be negligible. As more frames are trimmed, the impact becomes more significant on the ability of each classification model to correctly identify digits.
This study focuses on the effects of the trimming of frames of pixels, on the ability of the Recursive Partitioning and Classification Trees method, the Naive Bayes method, the k-Nearest Neighbor method and the Support Vector Machine method in the categorization of handwritten digits.
The results from the application of the k-Nearest Neighbour and Recursive Partitioning and Classification Trees methods exemplified the white noise effect in the trimming of the first few frames whilst the Naive Bayes and the Support Vector Machine did not. With respect to time all models saw a relative decrease in time from the initial dataset. The k-Nearest Neighbour method had the greatest decreases whilst the Support Vector Machine had significantly fluctuating times.
Cite This Paper
Jozette V. Roberts, Isaac Dialsingh, "On the Performance of Classification Techniques with Pixel Removal Applied to Digit Recognition", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.8, pp.38-47, 2016. DOI:10.5815/ijisa.2016.08.05
Reference
[1]Kumar, V. Vijaya, A. Srikrishna, B. Raveendra Babu, and M. Radhika Mani. 2010. "Classification and Recognition of Handwritten Digits by Using Mathematical Morphology." Sadhana 35, (4): 419-426.
[2]Huber, Roy A., and Alfred M. Headrick. 1999.“Handwriting Identification: Facts and Fundamentals.” New York: CRC Press.
[3]LeH, Theodora. 1954. "Six Basic Factors in Handwriting Classification." The Journal of Criminal Law, Criminology, and Police Science: 810-816.
[4]Livingston, Orville B. 1958. "Handwriting and Pen-Printing Classification System for Identifying Law Violators, A." J. Crim. L. Criminology & Police Sci. 49: 487.
[5]Leedham, Sargur Srihari Graham. 2003. "A Survey of Computer Methods in Forensic Handwritten Document Examination." Proceeding the Eleventh International Graphonomics Society Conference, Sccottsdale Arazona: 278 -281.
[6]Jain, Ramesh, Rangachar Kasturi, and Brian G. Schunck. 1995. “Machine Vision.” Vol.5. New York: McGraw-Hill.
[7]Giuliodori, Andrea, Rosa E. Lillo, and Daniel Peña. 2011. "Handwritten Digit Classification." Universidad Carlos III, Departamento de Estadística y Econometría.
[8]Jankowski, Norbert, and Krzysztof Grabczewski. 2007. "Handwritten Digit, Recognition Road to Contest victory." In Computational Intelligence and Data Mining: 491- 498.
[9]Kočenda, Evžen, and Alexandr Černý. 2014. “Elements of Time Series Econometrics: An Applied Approach, 2nd Edition.” Prague: Karolinum Press.
[10]Izenman, A. J. 2008. “Modern Multivariate Statistical Techniques, Regression, Classification, and Manifold Learning”. New York: Springer.
[11]Torgo, Luís Fernando Raínho Alves. 1999. "Inductive Learning of Tree-Based Regression Models." Universidade do Porto Reitoria.
[12]McCallum, Andrew, and Kamal Nigam. 1998. "A Comparison of Event Models for Naive Bayes Text Classification." In Workshop on Learning for Text Categorization, vol. 752: 41-48.
[13]Ade, Roshani, and P. R. Deshmukh. 2014. "Instance-Based Vs Batch-Based Incremental Learning Approach for Students Classification." International Journal of Computer Applications 106 (3): 37- 40
[14]Bennett, Kristin P., and Colin Campbell. 2000."Support Vector Machines: Hype or Hallelujah?" ACM SIGKDD Explorations Newsletter 2 (2): 1-13.
[15]Metsis, Vangelis, Ion Androutsopoulos, and Georgios Paliouras. 2006. "Spam Filtering with Naive Bayes-Which Naive Bayes?" In CEAS: 27-28.
[16]Zhang, Harry. 2004. "The Optimality of Naive Bayes." AA 1, ( 2): 3.
[17]Nopsuwanchai, Roongroj. 2004. "Discriminative Training Methods and Their Applications to Handwriting Recognition." PhD diss., University of Cambridge.
[18]Entezari-Maleki, Reza, Arash Rezaei, and Behrouz Minaei-Bidgoli. 2009 "Comparison of Classification Methods Based on the Type of Attributes and Sample Size." Journal of Convergence Information Technology 4 (3): 94-102.
[19]Oxhammar, Henrik. 2003. "Document Classification with Uncertain Data Using Statistical and Rule-Based Methods." Uppsala University: Department of Linguistics.
[20]Khamar, Khushbu. 2013. "Short Text Classification Using kNN Based on Distance Function." IJARCCE International Journal of Advanced Research in Computer and Communication Engineering. Government Engineering College, Modasa.
[21]Palacios-Alonso, Miguel A., Carlos A. Brizuela, and Luis Enrique Sucar. 2008. "Evolutionary Learning of Dynamic Naive Bayesian Classifiers." In FLAIRS conference: 660 - 665.