Abstract:

With the development of internet technologies, there has been a significant increase in textual data. Automatic text classification approaches have become important in order for these textual data to become meaningful. Feature selection and feature weighting have an important place in automatic text classification approaches. In this study, the effect of feature weighting methods on local feature selection methods is examined in detail. Two different weighting methods, three different local feature selection methods, three different criteria datasets, and two classifiers were used in the study. The highest Micro-F1 and Macro-F1 scores were 92.88 and 65.55 for the Reuters-21578 dataset, 99.02 and 98.15 for the 20Newsgroup dataset, and 97.19 and 93.40 for the Enron1 dataset. Experimental results show that better results are obtained with the combination of Odds Ratio (OR) feature selection method, Term Frequency (TF) feature weighting and Support Vector Machine (SVM) classifier.

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