{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:03:52Z","timestamp":1763114632003,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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":[[2024,10,25]]},"DOI":"10.1145\/3704323.3704378","type":"proceedings-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T08:25:22Z","timestamp":1736238322000},"page":"120-128","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi Dimensional Deep Encoding for Categorical Feature Space"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3154-9401","authenticated-orcid":false,"given":"Madhavi","family":"Alamuri","sequence":"first","affiliation":[{"name":"School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana State, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2204-0890","authenticated-orcid":false,"given":"Bapiraju","family":"Surampudi","sequence":"additional","affiliation":[{"name":"Cognitive Science Lab, International Institute of Information Technology, Hyderabad, Telangana State, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-130X","authenticated-orcid":false,"given":"Atul","family":"Negi","sequence":"additional","affiliation":[{"name":"School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana State, India"}]}],"member":"320","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Hussain Alkharusi. 2012. Categorical variables in regression analysis: A comparison of dummy and effect coding. International Journal of Education 4 2 (2012) 202.","DOI":"10.5296\/ije.v4i2.1962"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Kenneth\u00a0J Berry Paul\u00a0W Mielke\u00a0Jr and Hariharan\u00a0K Iyer. 1998. Factorial designs and dummy coding. Perceptual and motor skills 87 3 (1998) 919\u2013927.","DOI":"10.2466\/pms.1998.87.3.919"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Patricio Cerda Ga\u00ebl Varoquaux and Bal\u00e1zs K\u00e9gl. 2018. Similarity encoding for learning with dirty categorical variables. Machine Learning 107 8-10 (2018) 1477\u20131494.","DOI":"10.1007\/s10994-018-5724-2"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Craig A\u00a0Wendorf Craig A.\u00a0Wendorf. 2004. Primer on multiple regression coding: Common forms and the additional case of repeated contrasts. Understanding Statistics 3 1 (2004) 47\u201357.","DOI":"10.1207\/s15328031us0301_3"},{"key":"e_1_3_3_1_6_2","unstructured":"Fran\u00e7ois De\u00a0La\u00a0Bourdonnaye and Fabrice Daniel. 2021. Evaluating categorical encoding methods on a real credit card fraud detection database. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2112.12024 (2021)."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44989-2_43"},{"key":"e_1_3_3_1_8_2","unstructured":"Margherita Grandini Enrico Bagli and Giorgio Visani. 2020. Metrics for multi-class classification: an overview. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2008.05756 (2020)."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"John\u00a0T Hancock and Taghi\u00a0M Khoshgoftaar. 2020. Survey on categorical data for neural networks. Journal of Big Data 7 1 (2020) 1\u201341.","DOI":"10.1186\/s40537-020-00305-w"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Mohammad Hossin and Md\u00a0Nasir Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process 5 2 (2015) 1.","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"e_1_3_3_1_11_2","volume-title":"Data mining with decision trees: theory and applications","author":"Maimon Oded\u00a0Z","year":"2014","unstructured":"Oded\u00a0Z Maimon and Lior Rokach. 2014. Data mining with decision trees: theory and applications. Vol.\u00a081. World scientific."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Daniele Micci-Barreca. 2001. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. ACM SIGKDD Explorations Newsletter 3 1 (2001) 27\u201332.","DOI":"10.1145\/507533.507538"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85529-1_14"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Florian Pargent Florian Pfisterer Janek Thomas and Bernd Bischl. 2022. Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics 37 5 (2022) 2671\u20132692.","DOI":"10.1007\/s00180-022-01207-6"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Kedar Potdar Taher\u00a0S Pardawala and Chinmay\u00a0D Pai. 2017. A comparative study of categorical variable encoding techniques for neural network classifiers. International journal of computer applications 175 4 (2017) 7\u20139.","DOI":"10.5120\/ijca2017915495"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553516"},{"key":"e_1_3_3_1_17_2","unstructured":"Harry Zhang. 2004. The optimality of naive Bayes. Aa 1 2 (2004) 3."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Guoqiang Zhong Li-Na Wang Xiao Ling and Junyu Dong. 2016. An overview on data representation learning: From traditional feature learning to recent deep learning. The Journal of Finance and Data Science 2 4 (2016) 265\u2013278.","DOI":"10.1016\/j.jfds.2017.05.001"}],"event":{"name":"ICCPR 2024: 2024 13th International Conference on Computing and Pattern Recognition","acronym":"ICCPR 2024","location":"Tianjin China"},"container-title":["Proceedings of the 2024 13th International Conference on Computing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704323.3704378","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3704323.3704378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:05Z","timestamp":1750295885000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704323.3704378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":17,"alternative-id":["10.1145\/3704323.3704378","10.1145\/3704323"],"URL":"https:\/\/doi.org\/10.1145\/3704323.3704378","relation":{},"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"2025-01-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}