{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:40:05Z","timestamp":1755913205379,"version":"3.44.0"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tai.2024.3407034","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T13:51:40Z","timestamp":1717163500000},"page":"6146-6158","source":"Crossref","is-referenced-by-count":0,"title":["Curious Feature Selection-Based Clustering"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7512-3406","authenticated-orcid":false,"given":"Michal","family":"Moran","sequence":"first","affiliation":[{"name":"Curiosity Lab, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-7034","authenticated-orcid":false,"given":"Goren","family":"Gordon","sequence":"additional","affiliation":[{"name":"Curiosity Lab, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"84","article-title":"Tabular data: Deep learning is not all you need","volume-title":"Inf. Fusion","volume":"81","author":"Shwartz-Ziv","year":"2022"},{"issue":"6","key":"ref2","first-page":"7499","article-title":"Deep neural networks and tabular data: A survey","volume-title":"IEEE Trans. Neural Netw. Learn. Syst.","volume":"35","author":"Borisov","year":"2024"},{"issue":"2","key":"ref3","first-page":"9","article-title":"Challenges of feature selection for big data analytics","volume-title":"IEEE Intell. Syst.","volume":"32","author":"Li","year":"Mar.\/Apr."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1023\/a:1014043630878"},{"key":"ref5","first-page":"1157","article-title":"An introduction to variable and feature selection","volume-title":"J. Mach. Learn. Res.","volume":"3","author":"Guyon","year":"2003"},{"key":"ref6","first-page":"216","article-title":"On using supervised clustering analysis to improve classification performance","volume-title":"Inf. Sci.","volume":"454\u2013455","author":"Gan","year":"2018"},{"issue":"57","key":"ref7","first-page":"3","article-title":"Combining clustering with classification: A technique to improve classification accuracy","volume-title":"Lung Cancer","volume":"32","author":"Alapati","year":"2016"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.11591\/eei.v7i3.1272"},{"issue":"3","key":"ref9","first-page":"1174","article-title":"Deep curious feature selection: A recurrent, intrinsic-reward reinforcement learning approach to feature selection","volume-title":"IEEE Trans. Artif. Intell.","volume":"5","author":"Moran","year":"2024"},{"key":"ref10","first-page":"794","article-title":"Curious instance selection","volume-title":"Inf. Sci.","volume":"608","author":"Moran","year":"2022"},{"key":"ref11","first-page":"42","article-title":"Curious feature selection","volume-title":"Inf. Sci.","volume":"485","author":"Moran","year":"2019"},{"key":"ref12","first-page":"119","article-title":"Hierarchical curiosity loops and active sensing","volume-title":"Neural Netw.","volume":"32","author":"Gordon","year":"2012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.890271"},{"issue":"1","key":"ref14","first-page":"16","article-title":"A survey on feature selection methods","volume-title":"Comput. Electr. Eng.","volume":"40","author":"Chandrashekar","year":"2014"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3136625"},{"issue":"4","key":"ref16","first-page":"1060","article-title":"Stability of feature selection algorithm: A review","volume-title":"J. King Saud Univ. \u2014Comput. Inf. Sci.","volume":"34","author":"Khaire","year":"2022"},{"issue":"1","key":"ref17","first-page":"273","article-title":"Wrappers for feature subset selection","volume-title":"Artif. Intell.","volume":"97","author":"Kohavi","year":"1997"},{"key":"ref18","first-page":"178","article-title":"Filter methods for feature selection \u2013 A comparative study","volume-title":"in \"periodical\" Intell. Data Eng. Automated Learn. (IDEAL)","author":"S\u00e1nchez-Maro\u223cno","year":"2007"},{"key":"ref19","first-page":"37","article-title":"Feature selection for classification: A review","volume-title":"Data Classification","author":"Tang","year":"2014"},{"author":"Hall","key":"ref20","article-title":"Correlation-based feature selection for machine learning"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-57868-4_57"},{"issue":"8","key":"ref22","first-page":"1226","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume-title":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"27","author":"Peng","year":"2005"},{"issue":"1","key":"ref23","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Statist. Soc."},{"key":"ref24","first-page":"18","article-title":"LASSO: A feature selection technique in predictive modeling for machine learning","volume-title":"Proc. IEEE Int. Conf. Adv. Comput. Appl. (ICACA)","author":"Muthukrishnan","year":"2016"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-35488-8_6"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq134"},{"issue":"11","key":"ref27","first-page":"1119","article-title":"Floating search methods in feature selection","volume-title":"Pattern Recognit. Lett.","volume":"15","author":"Pudil","year":"1994"},{"volume-title":"Genetic Algorithms in Search, Optimization and Machine Learning","year":"1989","author":"Goldberg","key":"ref28"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2943928"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2227469"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2015.2476796"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.09.049"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3015756"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2020.2968743"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3100056"},{"key":"ref36","first-page":"70","article-title":"Feature selection in machine learning: A new perspective","volume-title":"Neurocomputing","volume":"300","author":"Cai","year":"2018"},{"issue":"1","key":"ref37","first-page":"1","article-title":"A fast clustering-based feature subset selection algorithm for high-dimensional data","volume-title":"IEEE Trans. Knowl. Data Eng.","volume":"25","author":"Song","year":"2013"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.5220\/0010259501220132"},{"issue":"3","key":"ref39","first-page":"301","article-title":"Unsupervised feature selection using feature similarity","volume-title":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"24","author":"Mitra","year":"2002"},{"key":"ref40","first-page":"9","article-title":"Unsupervised feature selection with feature clustering","volume-title":"Proc. IEEE\/WIC\/ACM Int. Conf. Web Intell. Intell. Agent Technol.","volume":"1","author":"Cheung","year":"2012"},{"key":"ref41","first-page":"104","article-title":"Integration of dense subgraph finding with feature clustering for unsupervised feature selection","volume-title":"Pattern Recog. Lett.","volume":"40","author":"Bandyopadhyay","year":"2014"},{"key":"ref42","first-page":"277","article-title":"An efficient unsupervised feature selection procedure through feature clustering","volume-title":"Pattern Recog. Lett.","volume":"131","author":"Yan","year":"2020"},{"key":"ref43","first-page":"1900","article-title":"A supervised feature selection method for mixed-type data using density-based feature clustering","volume-title":"Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC)","author":"Yan","year":"2021"},{"issue":"9","key":"ref44","article-title":"CGUFS: A clustering-guided unsupervised feature selection algorithm for gene expression data","volume-title":"Journal of King Saud University \u2014Comput. Inf. Sci.","volume":"35","author":"Xu","year":"2023"},{"issue":"5","key":"ref45","first-page":"1281","article-title":"An online unsupervised streaming features selection through dynamic feature clustering","volume-title":"IEEE Trans. Artif. Intell.","volume":"4","author":"Yan","year":"2023"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1023\/a:1007626913721"},{"issue":"3","key":"ref47","first-page":"408","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume-title":"IEEE Trans. Syst., Man, Cybern.","volume":"SMC-2","author":"Wilson","year":"1972"},{"key":"ref48","first-page":"768","article-title":"A density-based approach for instance selection","volume-title":"Proc. IEEE 27th Int. Conf. Tools With Artif. Intell. (ICTAI)","author":"Carbonera","year":"2015"},{"key":"ref49","first-page":"143","article-title":"Cluster-oriented instance selection for classification problems","volume-title":"Inf. Sci.","volume":"602","author":"Saha","year":"2022"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-25465-x_15"},{"issue":"8","key":"ref51","article-title":"The k-means algorithm: A comprehensive survey and performance evaluation","volume-title":"Electronics","volume":"9","author":"Ahmed","year":"2020"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1343"},{"key":"ref53","first-page":"232","article-title":"DBSCAN: Past, present and future","volume-title":"Proc. 5th Int. Conf. Appl. Digital Inf. Web Technol. (ICADIWT 2014)","author":"Khan","year":"2014"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-97864-2_4"},{"key":"ref55","first-page":"9861","article-title":"DeepDPM: Deep clustering with an unknown number of clusters","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recog.","author":"Ronen","year":"2022"},{"key":"ref56","first-page":"1596","article-title":"Deep adversarial subspace clustering","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognition","author":"Zhou","year":"2018"},{"issue":"2","key":"ref57","first-page":"52","article-title":"Subspace clustering","volume-title":"IEEE Signal Process. Mag.","volume":"28","author":"Vidal","year":"2011"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007731"},{"issue":"12","key":"ref59","first-page":"5509","article-title":"Deep subspace clustering","volume-title":"IEEE Trans. Neural Netw. Learn. Syst.","volume":"31","author":"Peng","year":"2020"},{"key":"ref60","first-page":"774","article-title":"Supervised clustering - algorithms and benefits","volume-title":"Proc. 16th IEEE Int. Conf. Tools Artif. Intell.","author":"Eick","year":"2004"},{"issue":"1","key":"ref61","volume-title":"Reinforcement Learning: An introduction","volume":"1","author":"Sutton","year":"1998"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27645-3_1"},{"volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","year":"2014","author":"Puterman","key":"ref63"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1201\/9781439821091"},{"article-title":"On-line Q-learning using connectionist systems","year":"1994","author":"Rummery","key":"ref65"},{"issue":"1","key":"ref66","first-page":"237","article-title":"Reinforcement Learning: A Survey","volume-title":"J. Artif. Int. Res.","volume":"4","author":"Kaelbling","year":"1996"},{"article-title":"Playing Atari with deep reinforcement learning","year":"2013","author":"Mnih","key":"ref67"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1201\/9781351006620-6"},{"issue":"3\u20134","key":"ref69","first-page":"219","article-title":"An introduction to deep reinforcement learning","volume-title":"Found. Trends\u00ae","volume":"11","author":"Francois-Lavet","year":"2018"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/tamd.2010.2056368"},{"issue":"38","key":"ref71","first-page":"12646","article-title":"Emergent exploration via novelty management","volume-title":"J. Neurosci.","volume":"34","author":"Gordon","year":"2014"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/s10827-014-0500-1"},{"article-title":"Computational complexity of gradient descent algorithm","year":"2021","author":"J","key":"ref73"},{"volume-title":"Learning Scikit-Learn: Machine Learning in Python","year":"2013","author":"Garreta","key":"ref74"},{"article-title":"UCI Machine Learning Repository","year":"2013","author":"Lichman","key":"ref75"},{"key":"ref76","article-title":"Medical appointment no shows"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10794552\/10542723.pdf?arnumber=10542723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:37Z","timestamp":1755911377000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":76,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3407034","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}