{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:54:43Z","timestamp":1702428883023},"reference-count":43,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82260849"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82260988"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61562045"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangxi University of Chinese Medicine Science and Technology Innovation Team Development Program","award":["CXTD22015"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection. The Gini index is an indicator used to evaluate the impurity of a dataset and is also commonly employed to measure the importance of features in feature selection. This article proposes a novel feature selection methodology based on these two concepts. In this methodology, we present the neighborhood Gini index and the neighborhood class Gini index and then extensively discuss their properties and relationships with attributes. Subsequently, two forward greedy feature selection algorithms are developed using these two metrics as a foundation. Finally, to comprehensively evaluate the performance of the algorithm proposed in this article, comparative experiments were conducted on 16 UCI datasets from various domains, including industry, food, medicine, and pharmacology, against four classical neighborhood rough set-based feature selection algorithms. The experimental results indicate that the proposed algorithm improves the average classification accuracy on the 16 datasets by over 6%, with improvements exceeding 10% in five. Furthermore, statistical tests reveal no significant differences between the proposed algorithm and the four classical neighborhood rough set-based feature selection algorithms. However, the proposed algorithm demonstrates high stability, eliminating most redundant or irrelevant features effectively while enhancing classification accuracy. In summary, the algorithm proposed in this article outperforms classical neighborhood rough set-based feature selection algorithms.<\/jats:p>","DOI":"10.7717\/peerj-cs.1711","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T08:38:19Z","timestamp":1702370299000},"page":"e1711","source":"Crossref","is-referenced-by-count":0,"title":["Feature selection based on neighborhood rough sets and Gini index"],"prefix":"10.7717","volume":"9","author":[{"given":"Yuchao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Bin","family":"Nie","sequence":"additional","affiliation":[]},{"given":"Jianqiang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Jiandong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yuwen","family":"Du","sequence":"additional","affiliation":[]},{"given":"Haike","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Xuepeng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xingxin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Miao","sequence":"additional","affiliation":[]}],"member":"4443","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.7717\/peerj-cs.1711\/ref-2","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","author":"Breiman","year":"1984","journal-title":"Classification and regression trees"},{"issue":"7","key":"10.7717\/peerj-cs.1711\/ref-3","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1007\/s00500-017-2672-x","article-title":"Generalized rough set models determined by multiple neighborhoods generated from a similarity relation","volume":"22","author":"Dai","year":"2018","journal-title":"Soft Computing"},{"key":"10.7717\/peerj-cs.1711\/ref-4","first-page":"1","article-title":"Statistical comparison of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-5","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","article-title":"A comparison of alternative tests of significance for the problem of m ranking","volume":"11","author":"Friedman","year":"1940","journal-title":"The Annals of Mathematical Statistics"},{"issue":"4","key":"10.7717\/peerj-cs.1711\/ref-6","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1007\/s10489-019-01597-z","article-title":"Feature redundancy term variation for mutual information-based feature selection","volume":"50","author":"Gao","year":"2020","journal-title":"Applied Intelligence"},{"issue":"4","key":"10.7717\/peerj-cs.1711\/ref-7","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2018.06.005","article-title":"Feature selection considering the composition of feature relevancy","volume":"112","author":"Gao","year":"2018","journal-title":"Pattern Recognition Letters"},{"key":"10.7717\/peerj-cs.1711\/ref-8","doi-asserted-by":"publisher","first-page":"115312","DOI":"10.1016\/j.eswa.2021.115312","article-title":"Hybrid filter-wrapper feature selection using whale optimization algorithm: a multi-objective approach","volume":"183","author":"Got","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-9","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/S0377-2217(98)00127-1","article-title":"Rough approximation of a preference relation by dominance relations","volume":"117","author":"Greco","year":"1999","journal-title":"European Journal of Operational Research"},{"issue":"18","key":"10.7717\/peerj-cs.1711\/ref-10","doi-asserted-by":"publisher","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"},{"issue":"9","key":"10.7717\/peerj-cs.1711\/ref-11","doi-asserted-by":"publisher","first-page":"10737","DOI":"10.1016\/j.eswa.2011.01.023","article-title":"Measuring relevance between discrete and continuous features based on neighborhood mutual information","volume":"38","author":"Hu","year":"2011","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"10.7717\/peerj-cs.1711\/ref-12","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.1109\/TFUZZ.2021.3093202","article-title":"Noise-tolerant fuzzy-beta-covering-based multigranulation rough sets and feature subset selection","volume":"30","author":"Huang","year":"2022","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.7717\/peerj-cs.1711\/ref-13","article-title":"The UCI machine learning repository","author":"Kelly","year":"1998"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-14","doi-asserted-by":"publisher","first-page":"107697","DOI":"10.1016\/j.patcog.2020.107697","article-title":"Stable feature selection using copula based mutual information","volume":"112","author":"Lall","year":"2021","journal-title":"Pattern Recognition"},{"issue":"6","key":"10.7717\/peerj-cs.1711\/ref-15","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1049\/el.2015.4172","article-title":"Feature selection based on geometric distance for high-dimensional data","volume":"52","author":"Lee","year":"2016","journal-title":"Electronics Letters"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1049\/ell2.12350","article-title":"Stable feature selection based on brain storm optimisation for high-dimensional data","volume":"58","author":"Li","year":"2021","journal-title":"Electronics Letters"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3390\/en11010185","article-title":"Feature reduction for power system transient stability assessment based on neighborhood rough set and discernibility matrix","volume":"11","author":"Li","year":"2018","journal-title":"Energies"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-18","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.chemolab.2016.07.009","article-title":"Neighborhood mutual information and its application on hyperspectral band selection for classification","volume":"157","author":"Liu","year":"2016","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"issue":"3","key":"10.7717\/peerj-cs.1711\/ref-19","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","article-title":"An embedded feature selection method for imbalanced data classification","volume":"6","author":"Liu","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-20","doi-asserted-by":"publisher","first-page":"e573","DOI":"10.7717\/peerj-cs.573","article-title":"Predicting defects in imbalanced data using resampling methods: an empirical investigation","volume":"8","author":"Malhotra","year":"2022","journal-title":"PeerJ Computer Science"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-21","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s11280-015-0381-x","article-title":"Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier","volume":"20","author":"Manek","year":"2017","journal-title":"World Wide Web-internet and Web Information Systems"},{"issue":"3","key":"10.7717\/peerj-cs.1711\/ref-22","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1007\/s12652-019-01364-5","article-title":"A novel hybrid wrapper-filter approach based on genetic algorithm, particle swarm optimization for feature subset selection","volume":"11","author":"Moslehi","year":"2020","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"4","key":"10.7717\/peerj-cs.1711\/ref-23","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1587\/transinf.E94.D.855","article-title":"Improved Gini-Index algorithm to correct feature-selection bias in text classification","volume":"E94-D","author":"Park","year":"2011","journal-title":"IEICE Transactions on Information and Systems"},{"issue":"5","key":"10.7717\/peerj-cs.1711\/ref-24","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","article-title":"Rough set","volume":"11","author":"Pawlak","year":"1982","journal-title":"International Journal of Information and Computer Science"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-25","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/S0165-0114(85)80029-4","article-title":"Rough sets and fuzzy sets","volume":"17","author":"Pawlak","year":"1985","journal-title":"Fuzzy Sets and Systems"},{"issue":"7","key":"10.7717\/peerj-cs.1711\/ref-26","doi-asserted-by":"publisher","first-page":"e1041","DOI":"10.7717\/peerj-cs.1041","article-title":"The accuracy of Random Forest performance can be improved by conducting a feature selection with a balancing strategy","volume":"8","author":"Prasetiyowati","year":"2022","journal-title":"PeerJ Computer Science"},{"issue":"12","key":"10.7717\/peerj-cs.1711\/ref-27","doi-asserted-by":"publisher","first-page":"5181","DOI":"10.1109\/TFUZZ.2022.3169625","article-title":"Feature selection considering multiple correlations based on soft fuzzy dominance rough sets for monotonic classification","volume":"30","author":"Sang","year":"2022","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"11","key":"10.7717\/peerj-cs.1711\/ref-28","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1007\/s13042-017-0729-x","article-title":"Decision-theoretic rough set model of multi-source decision systems","volume":"9","author":"Sang","year":"2018","journal-title":"International Journal of Machine Learning and Cybernetics"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-29","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/int.20051","article-title":"Dominance relation and rules in an incomplete ordered information system","volume":"20","author":"Shao","year":"2004","journal-title":"International Journal of Intelligent Systems"},{"issue":"4","key":"10.7717\/peerj-cs.1711\/ref-30","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1111\/coin.12072","article-title":"A fast and accurate feature selection algorithm based on binary consistency measure","volume":"32","author":"Shin","year":"2016","journal-title":"Computational Intelligence"},{"issue":"6","key":"10.7717\/peerj-cs.1711\/ref-31","doi-asserted-by":"publisher","first-page":"7310","DOI":"10.1007\/s10489-022-03770-3","article-title":"Information gain-based semi-supervised feature selection for hybrid data","volume":"53","author":"Shu","year":"2023","journal-title":"Applied Intelligence"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-32","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.neucom.2016.07.026","article-title":"A new hybrid filter-wrapper feature selection method for clustering based on ranking","volume":"214","author":"Solorio-Fern\u00e1ndez","year":"2016","journal-title":"Neurocomputing"},{"issue":"12","key":"10.7717\/peerj-cs.1711\/ref-33","doi-asserted-by":"publisher","first-page":"104942","DOI":"10.1016\/j.knosys.2019.104942","article-title":"Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems","volume":"186","author":"Sun","year":"2019a","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-34","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/e21020138","article-title":"A neighborhood rough sets-based attribute reduction method using Lebesgue and entropy measures","volume":"21","author":"Sun","year":"2019b","journal-title":"Entropy"},{"issue":"1","key":"10.7717\/peerj-cs.1711\/ref-35","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/TFUZZ.2022.3185285","article-title":"Feature grouping and selection with graph theory in robust fuzzy rough approximation space","volume":"31","author":"Wan","year":"2023","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-36","doi-asserted-by":"publisher","first-page":"4342","DOI":"10.3934\/math.2023216","article-title":"Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification","volume":"8","author":"Wang","year":"2023","journal-title":"AIMS Mathematics"},{"issue":"7","key":"10.7717\/peerj-cs.1711\/ref-37","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TNNLS.2017.2710422","article-title":"Feature selection based on neighborhood discrimination index","volume":"29","author":"Wang","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"9","key":"10.7717\/peerj-cs.1711\/ref-38","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1109\/TCYB.2019.2923430","article-title":"Feature selection based on neighborhood self-information","volume":"50","author":"Wang","year":"2019a","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"10.7717\/peerj-cs.1711\/ref-39","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ijar.2018.12.013","article-title":"Attribute reduction based on k-nearest neighborhood rough sets","volume":"106","author":"Wang","year":"2019b","journal-title":"International Journal of Approximate Reasoning"},{"issue":"5","key":"10.7717\/peerj-cs.1711\/ref-40","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1109\/TFUZZ.2022.3206508","article-title":"Feature selection with local density-based fuzzy rough set model for noisy data","volume":"31","author":"Yang","year":"2023","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"12","key":"10.7717\/peerj-cs.1711\/ref-41","doi-asserted-by":"publisher","first-page":"479289","DOI":"10.1155\/2014\/479289","article-title":"Feature selection with neighborhood entropy-based cooperative game theory","volume":"2014","author":"Zeng","year":"2014","journal-title":"Computational Intelligence and Neuroscience"},{"issue":"3","key":"10.7717\/peerj-cs.1711\/ref-42","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/s12539-020-00372-w","article-title":"Feature selection for microarray data classification using hybrid information gain and a modified binary Krill Herd algorithm","volume":"12","author":"Zhang","year":"2020","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"issue":"6","key":"10.7717\/peerj-cs.1711\/ref-43","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1109\/TFUZZ.2022.3216990","article-title":"Instance and feature selection using fuzzy rough sets: a bi-selection approach for data reduction","volume":"31","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Fuzzy Systems"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-1711.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1711.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1711.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1711.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T08:38:35Z","timestamp":1702370315000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-1711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":43,"alternative-id":["10.7717\/peerj-cs.1711"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.1711","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]},"article-number":"e1711"}}