Modified frequency-based term weighting schemes for text classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013621" target="_blank" >RIV/62690094:18450/17:50013621 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S156849461730251X?via%3Dihub" target="_blank" >http://www.sciencedirect.com/science/article/pii/S156849461730251X?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2017.04.069" target="_blank" >10.1016/j.asoc.2017.04.069</a>

Result language
angličtina
Original language name
Modified frequency-based term weighting schemes for text classification
Original language description
With the rapid growth of textual content on the Internet, automatic text categorization is a comparatively more effective solution in information organization and knowledge management. Feature selection, one of the basic phases in statistical-based text categorization, crucially depends on the term weighting methods In order to improve the performance of text categorization, this paper proposes four modified frequency-based term weighting schemes namely; mTF, mTFIDF, TFmIDF, and mTFmIDF. The proposed term weighting schemes take the amount of missing terms into account calculating the weight of existing terms. The proposed schemes show the highest performance for a SVM classifier with a micro-average F1 classification performance value of 97%. Moreover, benchmarking results on Reuters-21578, 20Newsgroups, and WebKB text-classification datasets, using different classifying algorithms such as SVM and KNN show that the proposed schemes mTF, mTFIDF, and mTFmIDF outperform other weighting schemes such as TF, TFIDF, and Entropy. Additionally, the statistical significance tests show a significant enhancement of the classification performance based on the modified schemes.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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