A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm

By Harun Uguz. Knowledge-Based Systems, 2011

495 citations recorded by the source corpus.

How researchers see this paper

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A novel probabilistic feature selection method for text classification (2012, unknown): “In text classification studies, though there are some hybrid approaches combining the filters and wrappers (Gunal, 2011; Uguz, 2011), commonly preferred feature selection methods are the filters thanks to their relatively low processing time.”
Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering (2017, background): “ch/Info/clef/, which consists of 571 words [20].”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, methodology): “We recall that the stem selection process based on IG values is a standard and effective method widely used in the literature [40], [42].”
An unsupervised feature selection algorithm based on ant colony optimization (2014, methodology): “Feature selection has been applied to many fields such as text categorization (Chen et al., 2006; Uğuz, 2011; Yang et al., 2011), face recognition (Kanan and Faez, 2008; Yan and Yuan, 2004), cancer classification (Guyon et al.”