Evaluation of Three Classifiers on the Letter Image Recognition Dataset

Evaluation of Three Classifiers on the Letter Image Recognition Dataset

Authors

  • Ridham Singhal Information Technology students at Maharaja Agrasen Institute of Technology

DOI:

https://doi.org/10.22377/ajcse.v2i2.49

Abstract

This report presents the Data Mining case study of the Letter Image Recognition Dataset available in UCI
Machine learning repository. The objective is to identify each of a large number of black-and-white
rectangular pixel displays as one of the 26 capital letters (26 classes) in the English alphabet. Three
different versatile classifiers namely Naïve Bayes, Decision tree C4.5 (J48) and Random Forest were
used to mine the data. Data mining open source tool WEKA 3.8.1 is used for the preprocessing and the
mining purposes.

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Published

2017-05-13

Issue

Vol. 2 No. 2 (2017): Asian Journal of Computer Science Engineering

Section

Research Article

License

Copyright (c) 2017 Ridham Singhal

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This is an Open Access article distributed under the terms of the Attribution-Noncommercial 4.0 International License [CC BY-NC 4.0], which requires that reusers give credit to the creator. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, for noncommercial purposes only.

How to Cite

[1]
“Evaluation of Three Classifiers on the Letter Image Recognition Dataset: Evaluation of Three Classifiers on the Letter Image Recognition Dataset”, ajcse, vol. 2, no. 2, pp. 01–04, May 2017, doi: 10.22377/ajcse.v2i2.49.