- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
기계학습에 기초한 자동분류의 성능 요소에 관한 연구
An Analytical Study on Performance Factors of Automatic Classification based on Machine Learning
초록
국내 학술회의 논문으로 구성된 문헌집합을 대상으로 기계학습에 기초한 자동분류의 성능에 영향을 미치는 요소들을 검토하였다. 특히 구현이 쉽고 컴퓨터 처리 속도가 빠른 로치오 알고리즘을 사용하여 『한국정보관리학회 학술대회 논문집』의 논문에 주제 범주를 자동 할당하는 분류 성능 측면에서 분류기 생성 방법, 학습집합 규모, 가중치부여 기법, 범주 할당 방법 등 주요 요소들의 특성을 다각적인 실험을 통해 살펴보았다. 결과적으로 분류 환경 및 문헌집합의 특성에 따라 파라미터(β, λ)와 학습집합의 크기(5년 이상)를 적절하게 적용하는 것이 효과적이며, 동등한 성능 수준이라면 보다 단순한 단일 가중치부여 기법을 사용하여 분류의 효율성을 높일 수 있음을 발견하였다. 또한 국내 학술회의 논문의 분류는 특정 논문에 하나 이상의 범주가 부여되는 복수-범주 분류(multi-label classification)가 실제 환경에 부합한다고 할 수 있으므로, 이러한 환경을 고려하여 주요 성능 요소들의 특성에 기초한 최적의 분류 모델을 개발할 필요가 있다.
- keywords
- automatic classification, text categorization, performance factors, conference paper, rocchio algorithm, multi-label classification, machine learning, 자동분류, 텍스트 범주화, 성능 요소, 학술회의 논문, 로치오 알고리즘, 복수-범주 분류, 기계학습
Abstract
This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (β, λ) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.
- keywords
- automatic classification, text categorization, performance factors, conference paper, rocchio algorithm, multi-label classification, machine learning, 자동분류, 텍스트 범주화, 성능 요소, 학술회의 논문, 로치오 알고리즘, 복수-범주 분류, 기계학습
참고문헌
강승식. (2002). 한국어 형태소 분석과 정보검색:홍릉출판사.
김성희. (2008). 기계학습을 이용한 문서 자동분류에 관한 연구. Journal of Information Science Theory and Practice, 39(4), 47-66.
김용환. (2012). 위키피디아를 이용한 분류자질 선정에 관한 연구. 정보관리학회지, 29(2), 155-171. http://dx.doi.org/10.3743/KOSIM.2012.29.2.155.
김종민. (2014). 특징 추출 비용에 민감한 분류를 위한 선형 분류기 최적화 알고리즘 (2021-2024). 2014년도 대한전자공학회 하계학술대회 논문집.
김판준. (2006). 기계학습을 통한 디스크립터 자동부여에 관한 연구. 정보관리학회지, 23(1), 279-299.
김판준. (2006). 로치오 알고리즘을 이용한 학술지 논문의 디스크립터 자동부여에 관한 연구. 정보관리학회지, 23(3), 69-90.
김판준. (2008). 용어 가중치부여 기법을 이용한 로치오 분류기의 성능 향상에 관한 연구. 정보관리학회지, 25(1), 211-233.
김판준. (2007). 문헌간 유사도를 이용한 자동분류에서 미분류 문헌의 활용에 관한 연구. 정보관리학회지, 24(1), 251-271.
김판준. (2012). 디스크립터 자동 할당을 위한 저자키워드의 재분류에 관한 실험적 연구. 정보관리학회지, 29(2), 225-246. http://dx.doi.org/10.3743/KOSIM.2012.29.2.225.
김판준. (2014). 해외 데이터베이스의 통제키워드에 기초한 국내 학술지 논문의 자동분류 성능 향상에 관한 실험적 연구. 한국문헌정보학회지, 48(3), 491-510. http://dx.doi.org/10.4275/KSLIS.2014.48.3.491.
송성전. (2012). 용어의 문맥활용을 통한 문헌 자동 분류의 성능 향상에 관한 연구. 정보관리학회지, 29(2), 205-224. http://dx.doi.org/10.3743/KOSIM.2012.29.2.205.
심경. (2006). 문헌범주화에서 학습문헌수 최적화에 관한 연구. 정보관리학회지, 23(4), 277-294.
심경. (2006). 학습문헌집합에 기 부여된 범주의 정확성과 문헌 범주화 성능. 정보관리학회지, 23(2), 265-285.
이용구. (2009). 기계번역을 이용한 교차언어 문서 범주화의 분류 성능 분석. 한국문헌정보학회지, 43(1), 313-332.
이용구. (2013). 문헌빈도와 장서빈도를 이용한 kNN 분류기의 자질선정에 관한 연구. 한국도서관·정보학회지, 44(1), 27-47.
이재윤. (2005). 문서측 자질선정을 이용한 고속 문서분류기의 성능향상에 관한 연구. Journal of Information Science Theory and Practice, 36(4), 51-69.
이재윤. (2005). 자질 선정 기준과 가중치 할당 방식간의 관계를 고려한 문서 자동분류의 개선에 대한 연구. 한국문헌정보학회지, 39(2), 123-146.
정은경. (2009). 문서범주화 성능 향상을 위한 의미기반 자질확장에 관한 연구. 정보관리학회지, 26(3), 261-278.
(2015). 한국연구재단 학술연구분야 분류표. http://www.nrf.re.kr.
(2016). 한국학술지인용색인 웹사이트. https://www.kci.go.kr.
AI-Salemi, B.. (2015). Boosting algorithms with topic modeling for multi-label text categorization: A comparative empirical study. Journal of Information Science, 41(5), 732-746. http://dx.doi.org/10.1177/0165551515590079.
Aliferis, C. F.. (2010). Local causal and markov blanket induction for causal discovery and feature selection for classification. Part I: Algorithms and empirical evaluation. Journal of Machine Learning Research, 11, 171-234.
Aliferis, C. F.. (2010). Local causal and markov blanket induction for causal discovery and feature selection for classification. Part II: Analysis and extensions. Journal of Machine Learning Research, 11, 235-284.
Aphinyanaphongs, Y.. (2014). A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization. Journal of the Association for Information Science and Technology, 65(10), 1964-1987. http://dx.doi.org/10.1002/asi.23110.
Chen, E.. (2011). Exploiting probabilistic topic models to improve text categorization under class imbalance. Information Processing and Management, 47(2), 202-214. http://dx.doi.org/10.1016/j.ipm.2010.07.003.
Cohen, W. W.. (1999). Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems, 17(2), 141-173. http://dx.doi.org/10.1145/306686.306688.
Debole, F.. (2003). Supervised term weighting for automated text categorization (784-788). Proceedings of the 18th ACM Symposium on Applied Computing (SAC) 2003.
Devi, P. R.. (2015). Multi-label learning with class-based features using extended centroid-based classification technique (CCBF). Procedia Computer Science, 54, 405-411. http://dx.doi.org/10.1016/j.procs.2015.06.047.
Forman, G.. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289-1305.
Forman, G.. (2008). Extremely fast text feature extraction for classification and indexing (26-30). Proceedings of the 17th ACM Conference on Information and Knowledge Mining (CIKM) 2008.
Foulds, J.. (2010). A review of multi-instance learning assumptions. Knowledge Engineering Review, 25(1), 1-25. http://dx.doi.org/10.1017/s026988890999035x.
Genkin, A.. (2007). Large-scale bayesian logistic regression for text categorization. Technometrics, 49(3), 291-304. http://dx.doi.org/10.1198/004017007000000245.
Harish B. S.. (2010). Representation and classification of text documents: A brief review (110-119). Proceedings of the IJCA Special Issue on Recent Trends in Image Processing and Pattern Recognition.
Hull, D. A.. (1994). Improving text retrieval for the routing problem using latent semantic indexing (282-291). SIGIR-94.
Ittner, J. D.. (1995). Text categorization of low quality images (301-315). Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval (SDAIR) 1995.
Jain, R.. (2015). Feature selection for effective text classification using semantic information. International Journal of Computer Applications, 113(10), 18-25. http://dx.doi.org/10.5120/19861-1818.
Jiang, S.. (2012). An improved k-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509. http://dx.doi.org/10.1016/j.eswa.2011.08.040.
Joachims, T.. (1997). A probabilistic analysis of the rocchio algorithm with tdf for text categorization (143-151). Proceedings of the International Conference on Machine Learning (ICML) 1997.
Khan, A.. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1(1), 4-20. http://dx.doi.org/10.4304/jait.1.1.4-20.
Kohavi, R.. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273-324. http://dx.doi.org/10.1016/s0004-3702(97)00043-x.
Korde, V.. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2), 85-99.
Kumar, M. A.. (2010). A comparison study on multiple binary-class SVM methods for unilabel text categorization. Pattern Recognition Letters, 31(11), 1437-1444. http://dx.doi.org/10.1016/j.patrec.2010.02.015.
Li, C. H.. (2009). An efficient document classification model using an improved back propagation neural network and singular value decomposition. Expert Systems with Applications, 36(2), 3208-3215. http://dx.doi.org/10.1016/j.eswa.2008.01.014.
Liu, Y.. (2007). Handling of imbalanced data in text classification: Category-based term weights. Natural Language Processing and Text Mining, , 171-192. http://dx.doi.org/10.1007/978-1-84628-754-1_10.
Moschitti, A.. (2003). Study on optimal parameter tuning for rocchio text classifier. Lecture Notes in Computer Science, (2633), 420-435. http://dx.doi.org/10.1007/3-540-36618-0_30.
Pang, G.. (2013). A generalized cluster centroid based classifier for text categorization. Information Processing and Management, 49(2), 576-586. http://dx.doi.org/10.1016/j.ipm.2012.10.003.
Patra, A.. (2013). A survey report on text classification with different term weighing methods and comparison between classification algorithms. International Journal of Computer Applications, 75(7), 14-18.
Read, J.. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333-359. http://dx.doi.org/10.1007/s10994-011-5256-5.
Rogati, M.. (2002). High-performing feature selection for text classification (4-9). Proceedings of the 11th International Conference on Information and knowledge management (CIKM)2002.
Schapire, R. E.. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168.
Schapire, R. E.. (1998). Boosting and rocchio applied to text filtering (215-223). Proceedings of the 21st Annual International ACM SIGIR conference on research and development in information retrieval (SIGIR) 1998.
Sebastiani, F.. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
Singhal, A.. (1997). Learning routing queries in a query zone (25-32). Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 1997.
Sokolova, M.. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427-437. http://dx.doi.org/10.1016/j.ipm.2009.03.002.
Tan, S.. (2008). An improved centroid classifier for text categorization. Expert Systems with Applications, 35(1-2), 279-285. http://dx.doi.org/10.1016/j.eswa.2007.06.028.
Tarragó, D. S.. (2014). A multi-instance learning wrapper based on the Rocchio classifier for web index recommendation. Knowledge-Based Systems, 59, 173-181. http://dx.doi.org/10.1016/j.knosys.2014.01.008.
Torii, M.. (2011). An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics. International Journal of Medical Informatics, 80(1), 56-66. http://dx.doi.org/10.1016/j.ijmedinf.2010.10.015.
Tsoumakas, G.. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13. http://dx.doi.org/10.4018/jdwm.2007070101.
Uysal, A. K.. (2014). The impact of preprocessing on text classification. Information Processing and Management, 50(1), 104-112. http://dx.doi.org/10.1016/j.ipm.2013.08.006.
Villena-Román, J.. (2011). Hybrid approach combining machine learning and a rule-based expert system for text categorization (323-328). Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference.
Wu, C.. (2009). Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Systems with Applications, 36(3), 4321-4330. http://dx.doi.org/10.1016/j.eswa.2008.03.002.
Yang, Y.. (1999). An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1-2), 69-90.
Yang, Y.. (1999). A re-examination for text categorization methods (42-49). Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 1999.
Yang, Y.. (1997). A comparative study on feature selection in text categorization (412-420). Proceedings of the 14th International Conference on Machine Learning (ICML) 1997.
Yu, B.. (2008). Latent semantic analysis for text categorization using neural network. Knowledge-Based Systems, 21(8), 900-904. http://dx.doi.org/10.1016/j.knosys.2008.03.045.
Zeng, A.. (2011). A text classification algorithm based on rocchio and hierarchical clustering. Lecture Notes in Computer Science, , 432-439. http://dx.doi.org/10.1007/978-3-642-24728-6_59.
Zhang, W.. (2011). A comparative study of TF*IDF, LSI and multiwords for text classification. Expert Systems with Applications, 38(3), 2758-2765. http://dx.doi.org/10.1016/j.eswa.2010.08.066.
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