A novel probabilistic feature selection method for text classification

By Alper Kursat Uysal, Serkan Günal. Knowledge-Based Systems, 2012

327 citations recorded by the source corpus.

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Text mining for market prediction: A systematic review (2014, background): “Feature selection for text classification can also be done based on a filter-based probabilistic approach (Uysal & Gunal, 2012).”
An embedded feature selection method for imbalanced data classification (2019, background): “However, if a subset of features that are more related to minority class information are selected as training data, classification results may be biased toward the minority class, which is not desired in practice [12].”
The impact of preprocessing on text classification (2014, methodology): “In this sense, one of the widely used methods, namely chi-square (CHI2) (Uysal & Gunal, 2012), is employed in this paper to select informative features.”
Feature selection for text classification: A review (2018, background): “Uysal et al. [92] proposed another measure named Distinguishing Feature Selector (DFS), which relies on the same idea of selecting distinctive terms by taking into consideration several rules of how distinctive or irrelevant a term could be.”
A feature selection model based on genetic rank aggregation for text sentiment classification (2017, methodology): “Uysal and Gunal [10] developed a probabilistic feature selection method.”
Text feature extraction based on deep learning: a review (2017, methodology): “The comparison is carried out for different datasets, classification algorithms, and success measures [34].”