Modified frequency-based term weighting schemes for text classification

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Modified frequency-based term weighting schemes for text classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modified frequency-based term weighting schemes for text classification

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied soft computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

58

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

193-206

Kód UT WoS článku

000405457500015

EID výsledku v databázi Scopus

2-s2.0-85018921015