Evaluation of Three Classifiers on the Letter Image Recognition Dataset Evaluation of Three Classifiers on the Letter Image Recognition Dataset
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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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Research Article
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