Examining the Impact of Feature Selection Methods on Text Classification
| Autor: | Mehmet Fatih Karaca, Safak Bayir |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
060201 languages & linguistics
General Computer Science Computer science business.industry Dimensionality reduction Feature selection Pattern recognition 06 humanities and the arts 02 engineering and technology k-nearest neighbors algorithm Term (time) Support vector machine Dimension (vector space) Feature (computer vision) 0602 languages and literature 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 8 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2017.081250 |
| Popis: | Feature selection that aims to determine and select the distinctive terms representing a best document is one of the most important steps of classification. With the feature selection, dimension of document vectors are reduced and consequently duration of the process is shortened. In this study, feature selection methods were studied in terms of dimension reduction rates, classification success rates, and dimension reduction-classification success relation. As classifiers, kNN (k-Nearest Neighbors) and SVM (Support Vector Machines) were used. 5 standard (Odds Ratio-OR, Mutual Information-MI, Information Gain-IG, Chi-Square-CHI and Document Frequency-DF), 2 combined (Union of Feature Selections-UFS and Correlation of Union of Feature Selections-CUFS) and 1 new (Sum of Term Frequency-STF) feature selection methods were tested. The application was performed by selecting 100 to 1000 terms (with an increment of 100 terms) from each class. It was seen that kNN produces much better results than SVM. STF was found out to be the most successful feature selection considering the average values in both datasets. It was also found out that CUFS, a combined model, is the one that reduces the dimension the most, accordingly, it was seen that CUFS classify the documents more successfully with less terms and in short period compared to many of the standard methods. |
| Databáze: | OpenAIRE |
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