<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In our work, we propose an ensemble of local and global filter-based feature selection method to reduce the high dimensionality of feature space and increase accuracy of spam review classification. These selected features are then used for training various classifiers for spam detection. Experimental results with four classifiers on two available datasets of hotel reviews show that the proposed feature selector improves the performance of spam classification in terms of well-known performance metrics such as AUC score.</p></abstract>

In our work, we propose an ensemble of local and global filter-based feature selection method to reduce the high dimensionality of feature space and increase accuracy of spam review classification. These selected features are then used for training various classifiers for spam detection. Experimental results with four classifiers on two available datasets of hotel reviews show that the proposed feature selector improves the performance of spam classification in terms of well-known performance metrics such as AUC score.

Spam Review Classification Using Ensemble of Global and Local Feature Selectors

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Spam Review Classification Using Ensemble of Global and Local Feature Selectors"}

In our work, we propose an ensemble of local and global filter-based feature selection method to reduce the high dimensionality of feature space and increase...

Spam Review Classification Using Ensemble of Global and Local Feature Selectors

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 29 - 42

Eingereicht: 15. Mai 2018

Akzeptiert: 21. Nov. 2018

DOI: https://doi.org/10.2478/cait-2018-0046

Schlüsselwörter
Feature selection, improved global feature selector, odds ratio, Spam classification

© 2018 Gunjan Ansari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Spam Review Classification Using Ensemble of Global and Local Feature Selectors

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 29 - 42

Eingereicht: 15. Mai 2018

Akzeptiert: 21. Nov. 2018

DOI: https://doi.org/10.2478/cait-2018-0046

SchlüsselwörterFeature selection, improved global feature selector, odds ratio, Spam classification

© 2018 Gunjan Ansari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Feature selection, improved global feature selector, odds ratio, Spam classification