{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:20Z","timestamp":1750220900308,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,12,9]]},"DOI":"10.1145\/3374549.3374574","type":"proceedings-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T10:23:34Z","timestamp":1580207014000},"page":"17-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Sweeper"],"prefix":"10.1145","author":[{"given":"Nutthawut","family":"Thawanthaleunglit","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunwadee","family":"Sripanidkulchai","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Journal of Engineering and Applied Sciences","author":"Alasadi Suad","year":"2017","unstructured":"Suad Alasadi , Review of Data Preprocessing Techniques in Data Mining , Journal of Engineering and Applied Sciences , Sep 2017 : 4102--4107. Suad Alasadi, Review of Data Preprocessing Techniques in Data Mining, Journal of Engineering and Applied Sciences, Sep 2017: 4102--4107."},{"key":"e_1_3_2_1_2_1","volume-title":"11th International Conference on Information Quality, MIT","author":"Sessions Valerie","year":"2016","unstructured":"Valerie Sessions and Marco Valtorta , The Effects of Data Quality on Machine Learning Algorithms , 11th International Conference on Information Quality, MIT , Cambridge, MA, USA , Nov 2016 . Valerie Sessions and Marco Valtorta, The Effects of Data Quality on Machine Learning Algorithms, 11th International Conference on Information Quality, MIT, Cambridge, MA, USA, Nov 2016."},{"volume-title":"IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence","author":"Patel Kayur","key":"e_1_3_2_1_3_1","unstructured":"Kayur Patel , Steven M. Drucker , James Fogarty , Ashish Kapoor , and Desney S . Tan, Using Multiple Models to Understand Data , IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence , 2011: 1723--1728. Kayur Patel, Steven M. Drucker, James Fogarty, Ashish Kapoor, and Desney S. Tan, Using Multiple Models to Understand Data, IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 2011: 1723--1728."},{"volume-title":"International Conference on Machine Learning and Computational Intelligence","author":"Puri Arjun","key":"e_1_3_2_1_4_1","unstructured":"Arjun Puri and Manoj Gupta , Review on Missing Value Imputation Techniques in Data Mining , International Conference on Machine Learning and Computational Intelligence , Sep 2017: 35--40. Arjun Puri and Manoj Gupta, Review on Missing Value Imputation Techniques in Data Mining, International Conference on Machine Learning and Computational Intelligence, Sep 2017: 35--40."},{"key":"e_1_3_2_1_5_1","first-page":"332","volume":"2013","author":"Abd Shaza M.","unstructured":"Shaza M. Abd Elrahman1 and Ajith Abraham , A Review of Class Imbalance Problem , Journal of Network and Innovative Computing , 2013 : 332 -- 340 . Shaza M. Abd Elrahman1 and Ajith Abraham, A Review of Class Imbalance Problem, Journal of Network and Innovative Computing, 2013: 332--340.","journal-title":"Journal of Network and Innovative Computing"},{"key":"e_1_3_2_1_6_1","volume-title":"Master of Science in Statistics University of California","author":"Huang Peng Jun","year":"2015","unstructured":"Peng Jun Huang , and Yingnian Wu , Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques , Master of Science in Statistics University of California , Los Angeles , 2015 . Peng Jun Huang, and Yingnian Wu, Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques, Master of Science in Statistics University of California, Los Angeles, 2015."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Bikesh Kumar Singh Kesari Verma and A. S. Thoke Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification International Journal of Computer Applications Arp 2015:  11--15.  Bikesh Kumar Singh Kesari Verma and A. S. Thoke Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification International Journal of Computer Applications Arp 2015: 11--15.","DOI":"10.5120\/20443-2793"},{"key":"e_1_3_2_1_8_1","volume-title":"IFAS","author":"Israel Glenn","year":"1992","unstructured":"Israel Glenn D , Determining Sample Size, Program Evaluation and Organizational Development , IFAS , University of Florida , 1992 : PEOD-6. Israel Glenn D, Determining Sample Size, Program Evaluation and Organizational Development, IFAS, University of Florida, 1992: PEOD-6."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622407.1622416"},{"volume-title":"International Joint Conference on Neural Networks","author":"He Haibo","key":"e_1_3_2_1_10_1","unstructured":"Haibo He , Yang Bai , Edwardo A. Garcia , and Shutao Li , ADASYN : Adaptive Synthetic Sampling Approach for Imbalanced Learning , International Joint Conference on Neural Networks , 2008: 1322--1328. Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li, ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning, International Joint Conference on Neural Networks, 2008: 1322--1328."},{"volume-title":"Pima Indians Diabetes Database. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database. Last access","year":"2019","key":"e_1_3_2_1_11_1","unstructured":"kaggle.com. Pima Indians Diabetes Database. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database. Last access : Aug 2, 2019 . kaggle.com. Pima Indians Diabetes Database. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database. Last access: Aug 2, 2019."},{"volume-title":"Breast Cancer Wisconsin (Diagnostic) Data Set. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/breast-cancer-wisconsin-data. Last access","year":"2019","key":"e_1_3_2_1_12_1","unstructured":"kaggle.com. Breast Cancer Wisconsin (Diagnostic) Data Set. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/breast-cancer-wisconsin-data. Last access : Aug 2, 2019 . kaggle.com. Breast Cancer Wisconsin (Diagnostic) Data Set. [Online]. Available from: https:\/\/www.kaggle.com\/uciml\/breast-cancer-wisconsin-data. Last access: Aug 2, 2019."},{"volume-title":"Titanic: Machine Learning from Disaster dataset. [Online]. Available from: https:\/\/www.kaggle.com\/c\/titanic\/data. Last access","year":"2019","key":"e_1_3_2_1_13_1","unstructured":"kaggle.com. Titanic: Machine Learning from Disaster dataset. [Online]. Available from: https:\/\/www.kaggle.com\/c\/titanic\/data. Last access : Aug 2, 2019 . kaggle.com. Titanic: Machine Learning from Disaster dataset. [Online]. Available from: https:\/\/www.kaggle.com\/c\/titanic\/data. Last access: Aug 2, 2019."}],"event":{"name":"ICSEB 2019: 2019 The 3rd International Conference on Software and e-Business","sponsor":["Waseda University Waseda University"],"location":"Tokyo Japan","acronym":"ICSEB 2019"},"container-title":["Proceedings of the 2019 3rd International Conference on Software and e-Business"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374549.3374574","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3374549.3374574","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:44Z","timestamp":1750203884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374549.3374574"}},"subtitle":["Automated Data Quality Processing and Model Generation for Data Classification"],"short-title":[],"issued":{"date-parts":[[2019,12,9]]},"references-count":13,"alternative-id":["10.1145\/3374549.3374574","10.1145\/3374549"],"URL":"https:\/\/doi.org\/10.1145\/3374549.3374574","relation":{},"subject":[],"published":{"date-parts":[[2019,12,9]]},"assertion":[{"value":"2020-01-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}