Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier
Railway safety is the core of railway transportation guarantee. The unstructured text data of railway safety problems is large, and the content of the text has no specific rules, which makes it very difficult to comprehensively analyze and solve the safety problems. Aiming at the intelligent classif...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2021-04-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000130584 |
| _version_ | 1818437747477577728 |
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| author | Gao Fan Wang Fuzhang Zhang Ming Zhao Junhua Li Gaoke |
| author_facet | Gao Fan Wang Fuzhang Zhang Ming Zhao Junhua Li Gaoke |
| author_sort | Gao Fan |
| collection | DOAJ |
| description | Railway safety is the core of railway transportation guarantee. The unstructured text data of railway safety problems is large, and the content of the text has no specific rules, which makes it very difficult to comprehensively analyze and solve the safety problems. Aiming at the intelligent classification of railway safety data, an evolutionary ensemble classifier model is proposed. By analyzing the characteristics of the catenary security issues of data, TF-IDF model is adopted to realize the feature extraction. Bagging ensemble classifier which uses Decision Tree as the base classifier classifies the text data, in the process of classification of Bagging, for the combined solution set of base classifier generated by Bagging Algorithm, Genetic Algorithm is proposed to optimize it to generate the combined solution set of base classifier with better classification results. Based on the safety problem of power supply contact network of a railway bureau, the experimental analysis shows that the TF-IDF+Bagging+Genetic Algorithm=Evolutionary Ensemble Classifier model has a high classification index in the text classification of railway safety problems. |
| first_indexed | 2024-12-14T17:29:35Z |
| format | Article |
| id | doaj.art-958f76a9747649aea0fc78d43b91648d |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-12-14T17:29:35Z |
| publishDate | 2021-04-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-958f76a9747649aea0fc78d43b91648d2022-12-21T22:53:08ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982021-04-01474717610.16157/j.issn.0258-7998.2002843000130584Text classification of railway safety fault based on TF-IDF evolutionary integrated classifierGao Fan0Wang Fuzhang1Zhang Ming2Zhao Junhua3Li Gaoke4China Academy of Railway Science，Beijing 100081，ChinaChina Academy of Railway Science，Beijing 100081，ChinaChina Academy of Railway Science，Beijing 100081，ChinaBeijing Jingwei Information Technologies Co.，Ltd.，Beijing 100081，ChinaChina Academy of Railway Science，Beijing 100081，ChinaRailway safety is the core of railway transportation guarantee. The unstructured text data of railway safety problems is large, and the content of the text has no specific rules, which makes it very difficult to comprehensively analyze and solve the safety problems. Aiming at the intelligent classification of railway safety data, an evolutionary ensemble classifier model is proposed. By analyzing the characteristics of the catenary security issues of data, TF-IDF model is adopted to realize the feature extraction. Bagging ensemble classifier which uses Decision Tree as the base classifier classifies the text data, in the process of classification of Bagging, for the combined solution set of base classifier generated by Bagging Algorithm, Genetic Algorithm is proposed to optimize it to generate the combined solution set of base classifier with better classification results. Based on the safety problem of power supply contact network of a railway bureau, the experimental analysis shows that the TF-IDF+Bagging+Genetic Algorithm=Evolutionary Ensemble Classifier model has a high classification index in the text classification of railway safety problems.http://www.chinaaet.com/article/3000130584software railway safety problemstf-idfbase classifierintegrated classifierevolutionary integration classifier |
| spellingShingle | Gao Fan Wang Fuzhang Zhang Ming Zhao Junhua Li Gaoke Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier Dianzi Jishu Yingyong software railway safety problems tf-idf base classifier integrated classifier evolutionary integration classifier |
| title | Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier |
| title_full | Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier |
| title_fullStr | Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier |
| title_full_unstemmed | Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier |
| title_short | Text classification of railway safety fault based on TF-IDF evolutionary integrated classifier |
| title_sort | text classification of railway safety fault based on tf idf evolutionary integrated classifier |
| topic | software railway safety problems tf-idf base classifier integrated classifier evolutionary integration classifier |
| url | http://www.chinaaet.com/article/3000130584 |
| work_keys_str_mv | AT gaofan textclassificationofrailwaysafetyfaultbasedontfidfevolutionaryintegratedclassifier AT wangfuzhang textclassificationofrailwaysafetyfaultbasedontfidfevolutionaryintegratedclassifier AT zhangming textclassificationofrailwaysafetyfaultbasedontfidfevolutionaryintegratedclassifier AT zhaojunhua textclassificationofrailwaysafetyfaultbasedontfidfevolutionaryintegratedclassifier AT ligaoke textclassificationofrailwaysafetyfaultbasedontfidfevolutionaryintegratedclassifier |