The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification
Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-I...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad-College of Science
2020
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30746/8/The%20Evaluation%20of%20Accuracy%20Performance.pdf |
| _version_ | 1796994492101820416 |
|---|---|
| author | Nur Syafiqah, Mohd Nafis Suryanti, Awang |
| author_facet | Nur Syafiqah, Mohd Nafis Suryanti, Awang |
| author_sort | Nur Syafiqah, Mohd Nafis |
| collection | UMP |
| description | Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document. |
| first_indexed | 2024-03-06T12:48:29Z |
| format | Article |
| id | UMPir30746 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:48:29Z |
| publishDate | 2020 |
| publisher | University of Baghdad-College of Science |
| record_format | dspace |
| spelling | UMPir307462024-01-05T08:26:02Z http://umpir.ump.edu.my/id/eprint/30746/ The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification Nur Syafiqah, Mohd Nafis Suryanti, Awang T Technology (General) Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document. University of Baghdad-College of Science 2020-12-31 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30746/8/The%20Evaluation%20of%20Accuracy%20Performance.pdf Nur Syafiqah, Mohd Nafis and Suryanti, Awang (2020) The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification. Iraqi Journal of Science, 61 (12). pp. 3397-3407. ISSN 0067-2904. (Published) https://doi.org/10.24996/ijs.2020.61.12.28 https://doi.org/10.24996/ijs.2020.61.12.28 |
| spellingShingle | T Technology (General) Nur Syafiqah, Mohd Nafis Suryanti, Awang The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title | The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title_full | The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title_fullStr | The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title_full_unstemmed | The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title_short | The evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| title_sort | evaluation of accuracy performance in an enhanced embedded feature selection for unstructured text classification |
| topic | T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/30746/8/The%20Evaluation%20of%20Accuracy%20Performance.pdf |
| work_keys_str_mv | AT nursyafiqahmohdnafis theevaluationofaccuracyperformanceinanenhancedembeddedfeatureselectionforunstructuredtextclassification AT suryantiawang theevaluationofaccuracyperformanceinanenhancedembeddedfeatureselectionforunstructuredtextclassification AT nursyafiqahmohdnafis evaluationofaccuracyperformanceinanenhancedembeddedfeatureselectionforunstructuredtextclassification AT suryantiawang evaluationofaccuracyperformanceinanenhancedembeddedfeatureselectionforunstructuredtextclassification |