{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:32:52Z","timestamp":1777696372514,"version":"3.51.4"},"reference-count":40,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:p>Feature selection has been shown to be a highly valuable strategy in data mining, pattern recognition, and machine learning. However, the majority of proposed feature selection methods do not account for feature interaction while calculating feature correlations. Interactive features are those features that have less individual relevance with the class, but can provide more joint information for the class when combined with other features. Inspired by it, a novel feature selection algorithm considering feature relevance, redundancy, and interaction in neighborhood rough set is proposed. First of all, a new method of information measurement called neighborhood symmetric uncertainty is proposed, to measure what proportion data a feature contains regarding category label. Afterwards, a new objective evaluation function of the interactive selection is developed. Then a novel feature selection algorithm named (NSUNCMI) based on measuring feature correlation, redundancy and interactivity is proposed. The results on the nine universe datasets and five representative feature selection algorithms indicate that NSUNCMI reduces the dimensionality of feature space efficiently and offers the best average classification accuracy.<\/jats:p>","DOI":"10.3233\/ida-216447","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:36:35Z","timestamp":1680867395000},"page":"345-359","source":"Crossref","is-referenced-by-count":6,"title":["A novel feature selection method considering feature interaction in neighborhood rough set"],"prefix":"10.1177","volume":"27","author":[{"given":"Wenjing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyong","family":"Ning","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China"},{"name":"Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216447_ref1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.eswa.2017.10.016","article-title":"Feature selection considering two types of feature relevancy and feature interdependency","volume":"93","author":"Hu","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-216447_ref2","first-page":"107149","article-title":"A new feature selection method based on symmetrical uncertainty and interaction gain","volume":"83","author":"Lin","year":"2019","journal-title":"Biology and Chemistry"},{"key":"10.3233\/IDA-216447_ref3","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.1016\/j.patcog.2015.02.025","article-title":"A novel feature selection method considering feature interaction","volume":"48","author":"Zeng","year":"2015","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-216447_ref4","doi-asserted-by":"crossref","first-page":"107167","DOI":"10.1016\/j.knosys.2021.107167","article-title":"A novel hybrid feature selection method considering feature interaction in neighborhood rough set","volume":"227","author":"Wan","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-216447_ref5","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","article-title":"Neighborhood rough set based heterogeneous feature subset selection","volume":"178","author":"Hu","year":"2008","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref6","doi-asserted-by":"crossref","first-page":"106439","DOI":"10.1016\/j.knosys.2020.106439","article-title":"Relevance assignation feature selection method based on mutual information for machine learning","author":"Gao","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-216447_ref7","doi-asserted-by":"crossref","first-page":"115365","DOI":"10.1016\/j.eswa.2021.115365","article-title":"A feature selection method via analysis of relevance, redundancy, and interaction","volume":"183","author":"Wang","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-216447_ref8","unstructured":"K. Kira, L. and A. Rendell, The feature selection problem: Traditional methods and a new algorithm, in: Proceedings of Ninth National Conference on Artificial Intelligence, 1992, pp. 129\u2013134."},{"key":"10.3233\/IDA-216447_ref9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and Empirical Analysis of Relief-F and RRelief-F","volume":"53","author":"M.","year":"2003","journal-title":"Machine Learning"},{"key":"10.3233\/IDA-216447_ref10","unstructured":"K. Kira and L.A. Rendell, The feature selection problem: Traditional methods and a new algorithm, in: Proceedings of Ninth National Conference on Artificial Intelligence, 1992, pp. 129\u2013134."},{"key":"10.3233\/IDA-216447_ref11","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0004-3702(03)00079-1","article-title":"Consistency-based search in feature selection","volume":"151","author":"Dash","year":"2003","journal-title":"Artificial Intelligence"},{"key":"10.3233\/IDA-216447_ref12","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/72.298224","article-title":"Using mutual information for selecting features in supervised neural net learning","volume":"5","author":"Roberto","year":"1994","journal-title":"IEEE Transactions on Neural Networks"},{"key":"10.3233\/IDA-216447_ref13","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/IDA-216447_ref14","doi-asserted-by":"crossref","first-page":"8520","DOI":"10.1016\/j.eswa.2015.07.007","article-title":"Feature selection using Joint Mutual Information Maximisation","volume":"42","author":"Mohamed","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-216447_ref15","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TKDE.2017.2650906","article-title":"Feature selection by maximizing independent classification information","volume":"29","author":"Wang","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/IDA-216447_ref16","first-page":"1531","article-title":"Fast binary feature selection with conditional mutual information","volume":"5","author":"Fleuret","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/IDA-216447_ref17","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1016\/j.patrec.2013.04.002","article-title":"Feature interaction maximization","volume":"34","author":"Bennasar","year":"2013","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/IDA-216447_ref18","doi-asserted-by":"crossref","unstructured":"D.D. Lewis, Feature Selection and Feature Extraction for Text Categorization, in: Proceedings of the Workshop on Speech and Natural Language, 1992, pp. 212\u2013217.","DOI":"10.3115\/1075527.1075574"},{"key":"10.3233\/IDA-216447_ref19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.knosys.2018.01.002","article-title":"High-dimensional hybrid feature selection using interaction information-guided search","volume":"145","author":"Nakariyakul","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-216447_ref20","doi-asserted-by":"crossref","unstructured":"G.H. John, R. Kohavi and K. Pfleger, Irrelevant features and the subset selection problem, in: Proceedings of the Eleventh International Conference on Machine Learning, 1994, pp. 121\u2013129.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"10.3233\/IDA-216447_ref21","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDA-216447_ref23","doi-asserted-by":"crossref","first-page":"113842","DOI":"10.1016\/j.eswa.2020.113842","article-title":"A feature selection algorithm of decision tree based on feature weight","volume":"164","author":"Zhou","year":"2021","journal-title":"Expert Systems With Applications"},{"key":"10.3233\/IDA-216447_ref24","doi-asserted-by":"crossref","first-page":"115365","DOI":"10.1016\/j.eswa.2021.115365","article-title":"A feature selection method via analysis of relevance, redundancy, and interaction","volume":"183","author":"Wang","year":"2021","journal-title":"Expert Systems With Applications"},{"key":"10.3233\/IDA-216447_ref25","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ins.2013.08.022","article-title":"Quick attribute reduct algorithm for neighborhood rough set model","volume":"271","author":"Liu","year":"2014","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref26","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ins.2021.10.026","article-title":"Dynamic interaction feature selection based on fuzzy rough set","volume":"581","author":"Wan","year":"2021","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref27","doi-asserted-by":"crossref","first-page":"106908","DOI":"10.1016\/j.knosys.2021.106908","article-title":"A novel approach to attribute reduction based on weighted neighborhood rough sets","volume":"220","author":"Hu","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-216447_ref28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2019.05.072","article-title":"Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification","volume":"502","author":"Sun","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref29","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1016\/j.asoc.2015.10.037","article-title":"Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments","volume":"38","author":"Apolloni","year":"2015","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-216447_ref30","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ins.2021.04.083","article-title":"Unsupervised attribute reduction for mixed data based on fuzzy rough sets","volume":"572","author":"Yuan","year":"2021","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2019.01.041","article-title":"Feature selection for imbalanced data based on neighborhood rough sets","volume":"483","author":"Chen","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref32","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.knosys.2018.03.015","article-title":"Attribute reduction based on max-decision neighborhood rough set model","volume":"151","author":"Fan","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-216447_ref34","doi-asserted-by":"crossref","unstructured":"S. Jiang and L. Wang, Efficient feature selection based on correlation measure between continuous and discrete features, Information Processing Letters 116 (2016), 203-215.","DOI":"10.1016\/j.ipl.2015.07.005"},{"key":"10.3233\/IDA-216447_ref35","first-page":"68","article-title":"Maximum relevance minimum common redundancy feature selection for nonlinear data","volume":"409","author":"J","year":"2017","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref36","doi-asserted-by":"crossref","first-page":"106299","DOI":"10.1016\/j.asoc.2020.106299","article-title":"Feature selection via normative fuzzy information weight with application into tumor classification","volume":"92","author":"Dai","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-216447_ref37","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.patrec.2005.09.004","article-title":"Information-preserving hybrid data reduction based on fuzzy-rough techniques","volume":"27","author":"Hu","year":"2006","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/IDA-216447_ref38","doi-asserted-by":"crossref","unstructured":"F. Nie, S. Yang, R. Zhang and X. Li, A general framework for auto-weighted feature selection via global redundancy minimization, IEEE Transactions on Image Processing 28 (2019), 2428\u20132438.","DOI":"10.1109\/TIP.2018.2886761"},{"key":"10.3233\/IDA-216447_ref39","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.eswa.2018.11.018","article-title":"Feature selection based on feature interactions with application to text categorization","volume":"120","author":"Tang","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-216447_ref40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2016.02.013","article-title":"Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy","volume":"56","author":"Zhang","year":"2016","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-216447_ref41","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.ins.2021.08.089","article-title":"New uncertainty measurement for categorical data based on fuzzy information structures: An application in attribute reduction","volume":"580","author":"Zhang","year":"2021","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-216447_ref42","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/TFUZZ.2017.2735947","article-title":"Streaming feature selection for multi-label learning based on fuzzy mutual information","volume":"25","author":"Lin","year":"2017","journal-title":"IEEE Transactions on Fuzzy Systems"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-216447","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:57Z","timestamp":1777454397000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-216447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,15]]},"references-count":40,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/ida-216447","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,15]]}}}