A novel feature selection algorithm for text categorization
By Wenqian Shang, Houkuan Huang, Haibin Zhu, Yongmin Lin, Youli Qu, Zhihai Wang. Expert systems with applications, 2007
377 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/9f027df81dbc5d0d10895d6faf167d8ea50cd073
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Parham Moradi
- Sina Tabakhi
- Fardin Akhlaghian Tab
- Alper Kursat Uysal
- Serkan Günal
- Fannia Pacheco
- Mathieu Gineste
- Ernesto Exposito
In Information Systems
- Alper Kursat Uysal
- Serkan Günal
- Fannia Pacheco
- Mathieu Gineste
- Sina Tabakhi
- Ernesto Exposito
- José Aguilar
- Parham Moradi
Citations in the corpus
- Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, methodology): “Other techniques categorized as filter are the Gini Index [17], the Information Gain [18], the Laplacian Score and the Sparsity Scores [19], [20], among others.”
- Text feature selection using ant colony optimization (2009, background): “Hence, we need to select some representative features from the original feature space to reduce the dimensionality of feature space and improve the efficiency and performance of classifier (Shang et al., 2007).”
- A hybrid approach of differential evolution and artificial bee colony for feature selection (2016, background): “A prominent problem of the classification tasks is the high dimensionality of the feature space (Shang et al., 2007).”
- A novel probabilistic feature selection method for text classification (2012, methodology): “Among all those techniques, chi square, information gain, Gini index, and deviation from Poisson distribution have been proven to be much more effective (Ogura, et al., 2009; Shang, et al., 2007; Yang & Pedersen, 1997).”
- An unsupervised feature selection algorithm based on ant colony optimization (2014, methodology): “There are numerous well-known univariate filter methods, such as information gain (Raileanu and Stoffel, 2004; Yu and Liu, 2003), gain ratio (Mitchell, 1997; Quinlan, 1986), symmetrical uncertainty (Biesiada and Duch, 2007; Yu and Liu, 2003), Gini index (Raileanu and Stoffel, 2004; Shang et al., 200”