Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification
The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To ran...
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2023
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| Online Access: | http://eprints.uthm.edu.my/10746/1/J16418_6a3a4efa584c2a61e9a08cd61b82225d.pdf http://eprints.uthm.edu.my/10746/ https://doi.org/10.1109/ACCESS.2023.3294563 |
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| author | ABDULKAREM ALSHALIF, SARAH SENAN, NORHALINA SAEED, FAISAL WAD GHABAN, WAD GHABAN IBRAHIM, NORAINI MUHAMMAD AAMIR, MUHAMMAD AAMIR WAREESA SHARIF, WAREESA SHARIF |
| author_facet | ABDULKAREM ALSHALIF, SARAH SENAN, NORHALINA SAEED, FAISAL WAD GHABAN, WAD GHABAN IBRAHIM, NORAINI MUHAMMAD AAMIR, MUHAMMAD AAMIR WAREESA SHARIF, WAREESA SHARIF |
| author_sort | ABDULKAREM ALSHALIF, SARAH |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To rank terms, most feature ranking algorithms, such as the Relative Discrimination Criterion (RDC) and Improved Relative Discrimination Criterion (IRDC), use document frequency (DF) and term frequency (TF). TF accepts the actual values of a term with frequently and rarely occurring terms used in existing feature ranking algorithms. However, these algorithms focus on the number of terms in a document rather than the number of terms in the category. In this research, an alternative method to RDC, called Alternative Relative Discrimination Criterion (ARDC) was proposed, which aims to improve the accuracy and effectiveness of RDC feature ranking. Specifically, ARDC is designed to identify terms commonly occurring in the positive class. The results obtained were compared to the existing RDC methods, which are RDC and IRDC, and standard benchmarking functions such as Information Gain (IG), Pearson Correlation Coefficient (PCC), and ReliefF. The experimental results reveal that using the suggested ARDC on the Reuters21578, 20newsgroup, and
TDT2 datasets provides better performance in terms of precision, recall, f-measure, and accuracy when employing well-known classifiers such as multinomial naïve Bayes (MNB), Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbor (KNN), and decision tree (DT). Another experiment was performed to validate the proposed technique, which aims to showcase the novelty of the ARDC approach. The experiment utilized the 20newsgroup dataset and employed the Relevant-Based Feature Ranking (RBFR) technique. Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) classifiers were used in this experiment to demonstrate the effectiveness of the suggested ARDC. |
| format | Article |
| id | my.uthm.eprints-10746 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | Ieee Acces |
| record_format | eprints |
| spelling | my.uthm.eprints-107462024-01-17T01:51:22Z http://eprints.uthm.edu.my/10746/ Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification ABDULKAREM ALSHALIF, SARAH SENAN, NORHALINA SAEED, FAISAL WAD GHABAN, WAD GHABAN IBRAHIM, NORAINI MUHAMMAD AAMIR, MUHAMMAD AAMIR WAREESA SHARIF, WAREESA SHARIF T Technology (General) The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To rank terms, most feature ranking algorithms, such as the Relative Discrimination Criterion (RDC) and Improved Relative Discrimination Criterion (IRDC), use document frequency (DF) and term frequency (TF). TF accepts the actual values of a term with frequently and rarely occurring terms used in existing feature ranking algorithms. However, these algorithms focus on the number of terms in a document rather than the number of terms in the category. In this research, an alternative method to RDC, called Alternative Relative Discrimination Criterion (ARDC) was proposed, which aims to improve the accuracy and effectiveness of RDC feature ranking. Specifically, ARDC is designed to identify terms commonly occurring in the positive class. The results obtained were compared to the existing RDC methods, which are RDC and IRDC, and standard benchmarking functions such as Information Gain (IG), Pearson Correlation Coefficient (PCC), and ReliefF. The experimental results reveal that using the suggested ARDC on the Reuters21578, 20newsgroup, and TDT2 datasets provides better performance in terms of precision, recall, f-measure, and accuracy when employing well-known classifiers such as multinomial naïve Bayes (MNB), Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbor (KNN), and decision tree (DT). Another experiment was performed to validate the proposed technique, which aims to showcase the novelty of the ARDC approach. The experiment utilized the 20newsgroup dataset and employed the Relevant-Based Feature Ranking (RBFR) technique. Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) classifiers were used in this experiment to demonstrate the effectiveness of the suggested ARDC. Ieee Acces 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10746/1/J16418_6a3a4efa584c2a61e9a08cd61b82225d.pdf ABDULKAREM ALSHALIF, SARAH and SENAN, NORHALINA and SAEED, FAISAL and WAD GHABAN, WAD GHABAN and IBRAHIM, NORAINI and MUHAMMAD AAMIR, MUHAMMAD AAMIR and WAREESA SHARIF, WAREESA SHARIF (2023) Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification. Digital Object Identifier, 11. pp. 71739-71755. https://doi.org/10.1109/ACCESS.2023.3294563 |
| spellingShingle | T Technology (General) ABDULKAREM ALSHALIF, SARAH SENAN, NORHALINA SAEED, FAISAL WAD GHABAN, WAD GHABAN IBRAHIM, NORAINI MUHAMMAD AAMIR, MUHAMMAD AAMIR WAREESA SHARIF, WAREESA SHARIF Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title | Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title_full | Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title_fullStr | Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title_full_unstemmed | Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title_short | Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification |
| title_sort | alternative relative discrimination criterion feature ranking technique for text classification |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10746/1/J16418_6a3a4efa584c2a61e9a08cd61b82225d.pdf http://eprints.uthm.edu.my/10746/ https://doi.org/10.1109/ACCESS.2023.3294563 |
| url_provider | http://eprints.uthm.edu.my/ |