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.

Downloads

Published

2017-05-13

How to Cite

Singhal, R. (2017). Evaluation of Three Classifiers on the Letter Image Recognition Dataset: Evaluation of Three Classifiers on the Letter Image Recognition Dataset. Asian Journal of Computer Science Engineering(AJCSE), 2(2), 01–04. https://doi.org/10.22377/ajcse.v2i2.49

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.