[논문]기계학습에 기초한 국내 학술지 논문의 자동분류에 관한 연구

김판준

doi:10.3743/kosim.2018.35.2.037

기계학습에 기초한 국내 학술지 논문의 자동분류에 관한 연구
An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning 원문보기

정보관리학회지 = Journal of the Korean society for information management, v.35 no.2 = no.108, 2018년, pp.37 - 62

초록
AI-Helper

문헌정보학 분야의 국내 학술지 논문으로 구성된 문헌집합을 대상으로 기계학습에 기초한 자동분류의 성능에 영향을 미치는 요소들을 검토하였다. 특히, "정보관리학회지"에 수록된 논문에 주제 범주를 자동 할당하는 분류 성능 측면에서 용어 가중치부여 기법, 학습집합 크기, 분류 알고리즘, 범주 할당 방법 등 주요 요소들의 특성을 다각적인 실험을 통해 살펴보았다. 결과적으로 분류 환경 및 문헌집합의 특성에 따라 각 요소를 적절하게 적용하는 것이 효과적이며, 보다 단순한 모델의 사용으로 상당히 좋은 수준의 성능을 도출할 수 있었다. 또한, 국내 학술지 논문의 분류는 특정 논문에 하나 이상의 범주를 할당하는 복수-범주 분류(multi-label classification)가 실제 환경에 부합한다고 할 수 있다. 따라서 이러한 환경을 고려하여 단순하고 빠른 분류 알고리즘과 소규모의 학습집합을 사용하는 최적의 분류 모델을 제안하였다.

Abstract ▼ AI-Helper

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

주제어

참고문헌 (54)

Kang, Seung-Shik (2002). Korean Morphology and Information Retrieval. Hongrung Publishing Company.
Kim, Seong-Hee, & Eom, Jae-Eun (2012). A study on the documents's automatic classification using machine learning. Journal of Information Management, 39(4), 47-66. http://dx.doi.org/10.1633/JIM.2008.39.4.047
Kim, Yong-Hwan, & Chung, Young-Mee (2012). An experimental study on feature selection using wikipedia for text categorization. Journal of the Korean Society for information Management, 29(2), 155-171. http://dx.doi.Org/10.3743/KOSIM.2012.29.2.155

원문보기 상세보기
Kim, Jong-Min, & Yoo, Chang D. (2014). Linear classifier optimization for feature acquisition cost-sensitive classification. In Proceedings of the IEEK Conference, 37(1), 2021-2024.
Kim, Pan Jun (2006a). A study on automatic assignment of descriptors using machine learning. Journal of the Korean Society for Information Management, 23(1), 279-299. http://dx.doi.org/10.3743/KOSIM.2006.23.1.279

원문보기 상세보기
Kim, Pan Jun (2006b). A study on the automatic descriptor assignment for scientific journal articles uing rocchio algorithm. Journal of the Korean Society for Information Management, 23(3), 69-89. http://dx.doi.org/10.3743/KOSIM.2006.23.3.069
Kim, Pan Jun (2008). A study on the performance improvement of rocchio classifier with term weighting methods. Journal of the Korean Society for Information Management, 25(1), 211-233. http://dx.doi.org/10.3743/KOSIM.2008.25.1.211

원문보기 상세보기
Kim, Pan Jun (2016). An analytical study on performance factors of automatic classification based on machine learning. Journal of the Korean Society for Information Management, 33(2), 33-59. http://dx.doi.org/10.3743/KOSIM.2016.33.2.033
Kim, Pan Jun, & Lee, Jae Yun (2007). Utilizing unlabeled documents in automatic classification with inter-document similarities. Journal of the Korean Society for Information Management, 24(1), 251-271. http://dx.doi.org/10.3743/KOSIM.2007.24.1.251

원문보기 상세보기
Kim, Pan Jun, & Lee, Jae Yun (2012). A study on the reclassification of author keywords for automatic assignment of descriptors. Journal of the Korean Society for Information Management, 29(2), 225-246. http://dx.doi.org/10.3743/KOSIM.2012.29.2.225

원문보기 상세보기
Kim, Pan Jun, & Lee, Jae Yun (2014). An experimental study on the performance improvement of automatic classification for the articles of korean journals based on controlled keywords in international database. Journal of the Korean Society for Library and Information Science, 48(3), 491-510. http://dx.doi.org/10.4275/KSLIS.2014.48.3.491

원문보기 상세보기
Song, Sung-Jeon, & Chung, Young-Mee (2012). A study on improving the performance of document classification using the context of terms. Journal of the Korean Society for Information Management, 29(2), 205-224. http://dx.doi.Org/10.3743/KOSIM.2012.29.2.205

원문보기 상세보기
Shim, Kyung (2006). Optimization of number of training documents in text categorization. Journal of the Korean Society for Information Management, 23(4), 277-294. http://dx.doi.org/10.3743/KOSIM.2006.23.4.277

원문보기 상세보기
Shim, Kyung, & Chung, Young-Mee (2006). The effect of the quality of pre-assigned subject categories on the text categorization performance. Journal of the Korean Society for Information Management, 23(2), 265-285. http://dx.doi.org/10.3743/KOSIM.2006.23.2.265

원문보기 상세보기
Lee, Yong-Gu (2009). Classification performance analysis of cross-language text categorization using machine translation. Journal of the Korean Society for Library and Information Science, 43(1), 313-332. http://dx.doi.org/10.4275/kslis.2009.43.1.313
Lee, Yong-Gu (2013). A study on feature selection for kNN classifier using document frequency and collection frequency. Journal of Korean Library and Information Science Society, 44(1), 27-47. http://dx.doi.org/10.16981/kliss.44.1.201303.27
Lee, Jae Yun (2005a). Improving the performance of a fast text classifier with document-side feature selection. Journal of Information Management, 36(4), 51-69. http://dx.doi.org/10.1633/jim.2005.36.4.051
Lee, Jae Yun (2005b). An empirical study on improving the performance of text categorization considering the relationships between feature selection criteria and weighting methods. Journal of the Korean Society for Library and Information Science, 39(2), 123-146. http://dx.doi.org/10.4275/kslis.2005.39.2.123

원문보기 상세보기
Chung, Eun-Kyung (2009). A semantic-based feature expansion approach for improving the effectiveness of text categorization by using wordNet. Journal of the Korean Society for Information Management, 26(3), 261-278. http://dx.doi.Org/10.3743/KOSIM.2009.26.3.261

원문보기 상세보기
National Research Foundation of Korea (2016). Research Field Classification Scheme. Retrieved from http://www.nrf.re.kr
Korea Citation Index (2018). Retrieved from https://www.kci.go.kr
AI-Salemi, B., Aziz, M., Juzaiddin, A., & Noah, S. (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

상세보기
Chen, E., Lin, Y., Xiong, H., Luo, Q., & Ma, H. (2011). Exploiting probabilistic topic models to improve text categorization under class imbalance. Information Processing and Management, 47(2), 202-214.

상세보기
Chen, Yao-Tsung, & Chen, Meng Chang (2011). Using chi-square statistics to measure similarities for text categorization. Expert Systems with Application, 38(4), 3085-3090.

상세보기
Dalal, M. K., & Zaveri, M. A. (2012). Automatic text classification of sports blog data, proceedings of the ieee international conference on computing, communications and applications (ComComAp 2012), Hong Kong, 11-13 January 2012, 219-222.
Dalal, M. K., & Zaveri, M. A. (2013). Automatic classification of unstructured blog text. Journal of Intelligent Learning Systems and Applications, 5(2), 108-114. http://dx.doi.Org/10.4236/jilsa.2013.52012.

상세보기
Eriksson, Tobias (2013). Automatic web page categorization using text classification methods. Master's Degree Project in Computer Science CSC School of Computer Science and Communication.
Foulds, J., & Frank, E. (2010). A review of multi-instance learning assumptions. Knowl. Eng. Rev., 25(1), 1-25.

상세보기
Hmeidi, I., Al-Ayyoub, M., Abdulla, N. A., Almodawar, A. A., Abooraig, R., & Mahyoub, N. A. (2015). Automatic arabic text categorization: A comprehensive comparative study. Journal of Information Science, 41(1), 114-124. https://doi.org/10.1177/0165551514558172

상세보기
Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved k-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509. https://doi.org/10.1016/j.eswa.2011.08.040

상세보기
Jindal, Rajni, Malhotra, Ruchika, & Jain, Abha. (2015). Techniques for text classification: Literature review and current trends. Webology, 12(2), 2-28.
Joorabchi, A., & Mahdi, A. E. (2011). An unsupervised approach to automatic classification of scientific literature utilizing bibliographic metadata. Journal of Information Science, 37(5), 499-514. https://doi.org/10.1177/0165551511417785

상세보기
Khan, A., Baharudin, B., & Lee, L. H. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1(1), 4-20. https://doi.org/10.4304/jait.1.1.4-20
Kumar, M. A., & Gopal, M. (2010). A comparison study on multiple binary-class SVM methods for unilabel text categorization. Pattern Recognition Letters, 31(11), 1437-1444. https://doi.org/10.1016/j.patrec.2010.02.015

상세보기
Li, C. H., & Park, S. C. (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. https://doi.org/10.1016/j.eswa.2008.01.014

상세보기
Liu, Y., Loh, H. T., Yousef-Toumi, K., & Tor, S. B. (2007). Handling of imbalanced data in text classification: category-based term weights. In Kao, A., & Poteet, S. R. eds. Natural Language Processing and Text Mining. Springer, 171-192. https://doi.org/10.1007/978-1-84628-754-1_10
Miao, Yun-Qian, & Kamel, Mohamed (2011). Pairwise optimized rocchio algorithm for text categorization. Pattern Recognition, 32(2), 375-382. https://doi.org/10.1016/j.patrec.2010.09.018

상세보기
Pawar, P. Y., & Gawande, S. H. (2012). Comparative study on different types of approaches to text categorization. International Journal of Machine Learning and Computing, 2(4), 423-426. https://doi.org/10.7763/ijmlc.2012.v2.158
Pedregosa, F. et al. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825-2830.
Read, J. (2010). Scalable Multi-label Classification (Thesis, Doctor of Philosophy (PhD)). University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/4645
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85, 333-359.

상세보기
Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39, 135-168.
Sebastiani, Fabrizio (2002). Machine learning in automated text categorization. ACM computing Surveys, 34(1), 1-47. https://doi.org/10.1145/505282.505283

상세보기
Shehab, M. A., Badarneh, O., Al-Ayyoub, M., & Jararweh, Y. (2016). A supervised approach for multi-label classification of Arabic news articles, 7th International Conference on Computer Science and Information Technology (CSIT), Amman, 2016, 1-6. http://dx.doi.Org/10.1109/CSIT.2016.7549465
Tarrago, D. S., Cornelis, C., Bello, R., & Herrera, F. (2014). A multi-instance learning wrapper based on the Rocchio classifier for web index recommendation. Knowledge-Based Systems, 59, 173-181. https://doi.org/10.1016/j.knosys.2014.01.008

상세보기
Torii, M., Yin, L., Nguyen, T., Mazumdar, C. T., Liu, H., Hartley, D. M., & Nelson, N. P. (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. https://doi.org/10.1016/j.ijmedinf.2010.10.015

상세보기
Tsoumakas G, Katakis I., & Vlahavas I. (2010). Mining multi-label data. In: Data mining and knowledge discovery handbook. Berlin: Springer, 667-685.
Uguz, Harun. (2011). A two-stage feature selection methods for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems, 24(7), 1024-1032. https://doi.org/10.1016/j.knosys.2011.04.014

상세보기
Vasuki, Vidya, & Cohen, Trevor (2010). Reflective random indexing for semi-automatic indexing of the biomedical literature. Journal of Biomedical Informatics, 43(5), 694-700. https://doi.org/10.1016/j.jbi.2010.04.001

상세보기
Villena-Roman, J., Collada-Perez, S., Lana-Serrano, S., & Gonzalez-Cristobal, J. C. (2011). Hybrid approach combining machine learning and a rule-based expert system for text categorization. In Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, 323-328.
Vogrincic, Sergeja, & Bosnic, Zoran (2011). Ontology-based multi-label classification of economic articles. ComSIS, 8(1), 101-119. https://doi.org/10.2298/csis100420034v

상세보기
Wang, Tai-Yue, & Chiang, Huei-Min (2007). Fuzzy support vector machine for multi-class text categorization. Information Processing and Management, 43(4), 914-929. https://doi.org/10.1016/j.ipm.2006.09.011

상세보기
Wu, Chih-Hung (2009). Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Systems with Applications, 36(1), 4321-4330. https://doi.org/10.1016/j.eswa.2008.03.002

상세보기
Yu, B., Xu, Zong-ben, & Li, Cheng-hua (2008). Latent semantic analysis for text categorization using neural network. Knowledge-Based Systems, 21(8), 900-904. https://doi.org/10.1016/j.knosys.2008.03.045

상세보기

저자의 다른 논문 :

LOADING...

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증