{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:14:22Z","timestamp":1784042062179,"version":"3.55.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National College Students\u2019 Innovation and Entrepreneurship Training Plan Program","award":["202410289027Z"],"award-info":[{"award-number":["202410289027Z"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["62076111"],"award-info":[{"award-number":["62076111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s13042-024-02406-x","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T15:01:57Z","timestamp":1728399717000},"page":"2523-2535","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Attribute reduction based on directional semi-neighborhood rough set"],"prefix":"10.1007","volume":"16","author":[{"given":"Damo","family":"Qian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"issue":"1\u20134","key":"2406_CR1","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S0020-0255(98)10006-3","volume":"111","author":"YY Yao","year":"1998","unstructured":"Yao YY (1998) Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci 111(1\u20134):239\u2013259. https:\/\/doi.org\/10.1016\/S0020-0255(98)10006-3","journal-title":"Inf Sci"},{"key":"2406_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121099","volume":"679","author":"CH Liu","year":"2024","unstructured":"Liu CH, Lin BW, Miao DQ (2024) A novel adaptive neighborhood rough sets based on sparrow search algorithm and feature selection. Inf Sci 679:121099. https:\/\/doi.org\/10.1016\/j.ins.2024.121099","journal-title":"Inf Sci"},{"key":"2406_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2024.108971","volume":"486","author":"XY Zhang","year":"2024","unstructured":"Zhang XY, Zhao WC (2024) Uncertainty measures and feature selection based on composite entropy for generalized multigranulation fuzzy neighborhood rough set. Fuzzy Sets Syst 486:108971. https:\/\/doi.org\/10.1016\/j.fss.2024.108971","journal-title":"Fuzzy Sets Syst"},{"key":"2406_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2023.108966","volume":"160","author":"HR Ju","year":"2023","unstructured":"Ju HR, Ding WP, Yang XB, Gu PP (2023) Bi-directional adaptive neighborhood rough sets based attribute subset selection. Int J Approximate Reasoning 160:108966. https:\/\/doi.org\/10.1016\/j.ijar.2023.108966","journal-title":"Int J Approximate Reasoning"},{"key":"2406_CR5","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1007\/s10489-015-0687-5","volume":"43","author":"BW Zhang","year":"2015","unstructured":"Zhang BW, Min F, Davide C (2015) Representative-based classification through covering-based neighborhood rough sets. Appl Intell 43:840\u2013854. https:\/\/doi.org\/10.1007\/s10489-015-0687-5","journal-title":"Appl Intell"},{"key":"2406_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.03.030","volume":"448\u2013449","author":"FL Liu","year":"2018","unstructured":"Liu FL, Zhang BW, Davide C, Wu WZ, Min F (2018) A comparison study of similarity measures for covering-based neighborhood classifiers. Inf Sci 448\u2013449:1\u201317. https:\/\/doi.org\/10.1016\/j.ins.2018.03.030","journal-title":"Inf Sci"},{"key":"2406_CR7","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.ins.2021.04.016","volume":"569","author":"TM Al-shami","year":"2021","unstructured":"Al-shami TM (2021) An improvement of rough sets\u2019 accuracy measure using containment neighborhoods with a medical application. Inf Sci 569:110\u2013124. https:\/\/doi.org\/10.1016\/j.ins.2021.04.016","journal-title":"Inf Sci"},{"key":"2406_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108127","volume":"114","author":"JC Bai","year":"2022","unstructured":"Bai JC, Sun BZ, Chu XL, Wang T, Li HT, Huang QC (2022) Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 114:108127. https:\/\/doi.org\/10.1016\/j.asoc.2021.108127","journal-title":"Appl Soft Comput"},{"key":"2406_CR9","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.ssci.2019.05.004","volume":"118","author":"D Liu","year":"2019","unstructured":"Liu D, Li JW (2019) Safety monitoring data classification method based on wireless rough network of neighborhood rough sets. Saf Sci 118:103\u2013108. https:\/\/doi.org\/10.1016\/j.ssci.2019.05.004","journal-title":"Saf Sci"},{"key":"2406_CR10","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/j.ins.2022.11.054","volume":"621","author":"GA Qi","year":"2023","unstructured":"Qi GA, Yang B, Li W (2023) Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making. Inf Sci 621:799\u2013843. https:\/\/doi.org\/10.1016\/j.ins.2022.11.054","journal-title":"Inf Sci"},{"issue":"1","key":"2406_CR11","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/JBHI.2022.3212479","volume":"27","author":"Y Xing","year":"2023","unstructured":"Xing Y, Kochunov P, Erp TGM, Ma TZ, Calhoun VD, Du YH (2023) A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia. IEEE J Biomed Health Inform 27(1):215\u2013226. https:\/\/doi.org\/10.1109\/JBHI.2022.3212479","journal-title":"IEEE J Biomed Health Inform"},{"key":"2406_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.ins.2022.12.074","volume":"624","author":"S An","year":"2023","unstructured":"An S, Guo XY, Wang CZ, Cuo G, Dai JH (2023) A soft neighborhood rough set model and its applications. Inf Sci 624:185\u2013199. https:\/\/doi.org\/10.1016\/j.ins.2022.12.074","journal-title":"Inf Sci"},{"key":"2406_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110632","volume":"274","author":"M Hu","year":"2023","unstructured":"Hu M, Guo YT, Chen DG, Tsang ECC, Zhang QS (2023) Attribute reduction based on neighborhood constrained fuzzy rough sets. Knowl-Based Syst 274:110632. https:\/\/doi.org\/10.1016\/j.knosys.2023.110632","journal-title":"Knowl-Based Syst"},{"key":"2406_CR14","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1007\/s10489-022-03366-x","volume":"53","author":"JH Liu","year":"2023","unstructured":"Liu JH, Lin YJ, Du JX, Zhang HB, Chen ZY, Zhang J (2023) ASFS: A novel streaming feature selection for multi-label data based on neighborhood rough set. Appl Intell 53:1707\u20131724. https:\/\/doi.org\/10.1007\/s10489-022-03366-x","journal-title":"Appl Intell"},{"key":"2406_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110366","volume":"143","author":"MAND Sewwandi","year":"2023","unstructured":"Sewwandi MAND, Li YF, Zhang JL (2023) A class-specific feature selection and classification approach using neighborhood rough set and k-nearest neighbor theories. Appl Soft Comput 143:110366. https:\/\/doi.org\/10.1016\/j.asoc.2023.110366","journal-title":"Appl Soft Comput"},{"key":"2406_CR16","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2022.11.060","volume":"621","author":"TY Yin","year":"2023","unstructured":"Yin TY, Chen HM, Yuan Z, Li TR, Liu KY (2023) Noise-resistant multilabel fuzzy neighborhood rough sets for feature subset selection. Inf Sci 621:200\u2013226. https:\/\/doi.org\/10.1016\/j.ins.2022.11.060","journal-title":"Inf Sci"},{"key":"2406_CR17","doi-asserted-by":"publisher","unstructured":"Huang ZH, Li JJ (2024) Covering based multi-granulation rough fuzzy sets with applications to feature selection. Expert Systems with Applications 238(Part C), 121908 https:\/\/doi.org\/10.1016\/j.eswa.2023.121908","DOI":"10.1016\/j.eswa.2023.121908"},{"key":"2406_CR18","doi-asserted-by":"publisher","unstructured":"Turaga VKH, Chebrolu S (2025) Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce. Expert Systems with Applications 260, 125323 https:\/\/doi.org\/10.1016\/j.eswa.2024.125323","DOI":"10.1016\/j.eswa.2024.125323"},{"key":"2406_CR19","doi-asserted-by":"publisher","unstructured":"Sang BB, Yang L, Chen HM, Xu WH, Zhang XY. Fuzzy rough feature selection using a robust non-linear vague quantifier for ordinal classification. Expert Systems with Applications 230, 120480 https:\/\/doi.org\/10.1016\/j.eswa.2023.120480","DOI":"10.1016\/j.eswa.2023.120480"},{"key":"2406_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102276","volume":"106","author":"PF Zhang","year":"2024","unstructured":"Zhang PF, Wang DX, Yu Z, Zhang YJ, Jiang T, Li TR (2024) A multi-scale information fusion-based multiple correlations for unsupervised attribute selection. Information Fusion 106:102276. https:\/\/doi.org\/10.1016\/j.inffus.2024.102276","journal-title":"Information Fusion"},{"key":"2406_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2024.108916","volume":"483","author":"YY Yang","year":"2024","unstructured":"Yang YY, Chen DG, Ji ZY, Zhang X, Dong LJ (2024) A two-way accelerator for feature selection using a monotonic fuzzy conditional entropy. Fuzzy Sets Syst 483:108916. https:\/\/doi.org\/10.1016\/j.fss.2024.108916","journal-title":"Fuzzy Sets Syst"},{"key":"2406_CR22","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1007\/s00500-021-06687-0","volume":"26","author":"YY Yang","year":"2022","unstructured":"Yang YY, Chen DG, Zhang X, Ji ZY (2022) Covering rough set-based incremental feature selection for mixed decision system. Soft Comput 26:2651\u20132669. https:\/\/doi.org\/10.1007\/s00500-021-06687-0","journal-title":"Soft Comput"},{"key":"2406_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111020","volume":"158","author":"QH Guo","year":"2025","unstructured":"Guo QH, Yang XB, Li M, Qian YH (2025) Collaborative graph neural networks for augmented graphs: A local-to-global perspective. Pattern Recogn 158:111020. https:\/\/doi.org\/10.1016\/j.patcog.2024.111020","journal-title":"Pattern Recogn"},{"key":"2406_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2023.108974","volume":"160","author":"Y Li","year":"2023","unstructured":"Li Y, Wu XX, Wang XZ (2023) Incremental reduction methods based on granular ball neighborhood rough sets and attribute grouping. Int J Approximate Reasoning 160:108974. https:\/\/doi.org\/10.1016\/j.ijar.2023.108974","journal-title":"Int J Approximate Reasoning"},{"key":"2406_CR25","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.inffus.2023.02.016","volume":"95","author":"L Sun","year":"2023","unstructured":"Sun L, Si SS, Ding WP, Wang XY, Xu JC (2023) TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Information Fusion 95:91\u2013108. https:\/\/doi.org\/10.1016\/j.inffus.2023.02.016","journal-title":"Information Fusion"},{"key":"2406_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2024.109256","volume":"173","author":"NN Thuy","year":"2024","unstructured":"Thuy NN, Wongthanavasu S (2024) Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets. Int J Approximate Reasoning 173:109256. https:\/\/doi.org\/10.1016\/j.ijar.2024.109256","journal-title":"Int J Approximate Reasoning"},{"key":"2406_CR27","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1016\/j.ins.2022.08.084","volume":"612","author":"FF Zhao","year":"2022","unstructured":"Zhao FF, Pang B, Mi JS (2022) A new approach to generalized neighborhood system-based rough sets via convex structures and convex matroids. Inf Sci 612:1187\u20131205. https:\/\/doi.org\/10.1016\/j.ins.2022.08.084","journal-title":"Inf Sci"},{"key":"2406_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107679","volume":"111","author":"L Yang","year":"2021","unstructured":"Yang L, Qin KY, Sang BB, Xu WH (2021) Dynamic fuzzy neighborhood rough set approach for interval-valued information systems with fuzzy decision. Appl Soft Comput 111:107679. https:\/\/doi.org\/10.1016\/j.asoc.2021.107679","journal-title":"Appl Soft Comput"},{"key":"2406_CR29","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.ins.2021.12.047","volume":"587","author":"Y Chen","year":"2022","unstructured":"Chen Y, Yang XB, Li JH, Wang PX, Qian YH (2022) Fusing attribute reduction accelerators. Inf Sci 587:354\u2013370. https:\/\/doi.org\/10.1016\/j.ins.2021.12.047","journal-title":"Inf Sci"},{"issue":"9","key":"2406_CR30","doi-asserted-by":"publisher","first-page":"9281","DOI":"10.1109\/TKDE.2022.3222447","volume":"35","author":"SY Xia","year":"2023","unstructured":"Xia SY, Wu SL, Chen XX, Wang GY, Gao XB, Zhang QH, Giem E, Chen ZZ (2023) GRRS: Accurate and efficient neighborhood rough set for feature selection. IEEE Trans Knowl Data Eng 35(9):9281\u20139294. https:\/\/doi.org\/10.1109\/TKDE.2022.3222447","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2406_CR31","doi-asserted-by":"publisher","unstructured":"Ju HR, Yin T, Huang JS, Ding WP, Yang XB (2023) Sparse mutual granularity-based feature selection and its application of schizophrenia patients. IEEE Transactions on Emerging Topics in Computational Intelligence, 1\u201311 https:\/\/doi.org\/10.1109\/TETCI.2023.3314548","DOI":"10.1109\/TETCI.2023.3314548"},{"key":"2406_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122324","volume":"238","author":"N Wang","year":"2024","unstructured":"Wang N, Zhao EH (2024) A new method for feature selection based on weighted $$k$$-nearest neighborhood rough set. Expert Syst Appl 238:122324. https:\/\/doi.org\/10.1016\/j.eswa.2023.122324","journal-title":"Expert Syst Appl"},{"key":"2406_CR33","doi-asserted-by":"publisher","first-page":"7635","DOI":"10.1038\/s41598-024-57547-4","volume":"14","author":"I Raza","year":"2024","unstructured":"Raza I, Jamal M, Qureshi R, Shahid A, Vistorte A, Samad M, Ashraf I (2024) Adaptive neighborhood rough set model for hybrid data processing: A case study on Parkinson\u2019s disease behavioral analysis. Sci Rep 14:7635. https:\/\/doi.org\/10.1038\/s41598-024-57547-4","journal-title":"Sci Rep"},{"key":"2406_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-024-05578-9","author":"DM Qian","year":"2024","unstructured":"Qian DM, Liu KY, Zhang SM, Yang XB (2024) Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-024-05578-9","journal-title":"Appl Intell"},{"key":"2406_CR35","doi-asserted-by":"publisher","unstructured":"Zhang PF, Li TR, Yuan Z, Luo C, Liu KY, Yang XL (2022) Heterogeneous feature selection based on neighborhood combination entropy. IEEE Transactions on Neural Networks and Learning Systems, 1\u201314 https:\/\/doi.org\/10.1109\/TNNLS.2022.3193929","DOI":"10.1109\/TNNLS.2022.3193929"},{"key":"2406_CR36","doi-asserted-by":"publisher","first-page":"10737","DOI":"10.1016\/j.eswa.2011.01.023","volume":"38","author":"QH Hu","year":"2011","unstructured":"Hu QH, Zhang L, Zhang D, Pan W, An S, Pedrycz W (2011) Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst Appl 38:10737\u201310750. https:\/\/doi.org\/10.1016\/j.eswa.2011.01.023","journal-title":"Expert Syst Appl"},{"key":"2406_CR37","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1007\/s13042-019-00997-4","volume":"11","author":"YY Yang","year":"2020","unstructured":"Yang YY, Song SJ, Chen DG, Zhang X (2020) Discernible neighborhood counting based incremental feature selection for heterogeneous data. Int J Mach Learn Cybern 11:1115\u20131127. https:\/\/doi.org\/10.1007\/s13042-019-00997-4","journal-title":"Int J Mach Learn Cybern"},{"key":"2406_CR38","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s13042-017-0758-5","volume":"10","author":"JZ Li","year":"2019","unstructured":"Li JZ, Yang XB, Song XN, Li JH, Wang PX, Yu DJ (2019) Neighborhood attribute reduction: A multi-criterion approach. Int J Mach Learn Cybern 10:731\u2013742. https:\/\/doi.org\/10.1007\/s13042-017-0758-5","journal-title":"Int J Mach Learn Cybern"},{"key":"2406_CR39","doi-asserted-by":"publisher","unstructured":"Ding WP, Sun Y, Li M, Liu J, Ju HR, Huang JS, Lin CT (2022) A novel spark-based attribute reduction and neighborhood classification for rough evidence. IEEE Transactions on Cybernetics, 1\u201314 https:\/\/doi.org\/10.1109\/TCYB.2022.3208130","DOI":"10.1109\/TCYB.2022.3208130"},{"key":"2406_CR40","doi-asserted-by":"publisher","unstructured":"Xu JC, Shen KL, Sun L (2022) Multi-label feature selection based on fuzzy neighborhood rough sets. Complex & Intelligent Systems 8, 2105\u20132129 https:\/\/doi.org\/10.1007\/s40747-021-00636-y","DOI":"10.1007\/s40747-021-00636-y"},{"key":"2406_CR41","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.inffus.2021.10.017","volume":"80","author":"PF Zhang","year":"2022","unstructured":"Zhang PF, Li TR, Yuan Z, Luo C, Wang GQ, Liu J, Du SD (2022) A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data. Information Fusion 80:87\u2013103. https:\/\/doi.org\/10.1016\/j.inffus.2021.10.017","journal-title":"Information Fusion"},{"issue":"9","key":"2406_CR42","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1109\/TCYB.2019.2923430","volume":"50","author":"CZ Wang","year":"2020","unstructured":"Wang CZ, Huang Y, Shao MW, Hu QH, Chen DG (2020) Feature selection based on neighborhood self-information. IEEE Transactions on Cybernetics 50(9):4031\u20134042. https:\/\/doi.org\/10.1109\/TCYB.2019.2923430","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"12","key":"2406_CR43","doi-asserted-by":"publisher","first-page":"6313","DOI":"10.1109\/TNNLS.2018.2830700","volume":"29","author":"A Mariello","year":"2018","unstructured":"Mariello A, Battiti R (2018) Feature selection based on the neighborhood entropy. IEEE Transactions on Neural Networks and Learning Systems 29(12):6313\u20136322. https:\/\/doi.org\/10.1109\/TNNLS.2018.2830700","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2406_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110679","volume":"146","author":"KY Liu","year":"2023","unstructured":"Liu KY, Li TR, Yang XB, Ju HR, Yang X, Liu D (2023) Feature selection in threes: Neighborhood relevancy, redundancy, and granularity interactivity. Appl Soft Comput 146:110679. https:\/\/doi.org\/10.1016\/j.asoc.2023.110679","journal-title":"Appl Soft Comput"},{"key":"2406_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121340","volume":"686","author":"KL Qu","year":"2025","unstructured":"Qu KL, Gao P, Dai Q, Shun Y, Hua X (2025) Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set. Inf Sci 686:121340. https:\/\/doi.org\/10.1016\/j.ins.2024.121340","journal-title":"Inf Sci"},{"key":"2406_CR46","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.eswa.2006.10.043","volume":"34","author":"QH Hu","year":"2008","unstructured":"Hu QH, Yu DR, Xie ZX (2008) Neighborhood classifiers. Expert Syst Appl 34:866\u2013876. https:\/\/doi.org\/10.1016\/j.eswa.2006.10.043","journal-title":"Expert Syst Appl"},{"key":"2406_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01999-z","author":"SZ Wu","year":"2023","unstructured":"Wu SZ, Wang LT, Ge SY, Hao ZW, Liu YL (2023) Neighborhood rough set with neighborhood equivalence relation for feature selection. Knowl Inf Syst. https:\/\/doi.org\/10.1007\/s10115-023-01999-z","journal-title":"Knowl Inf Syst"},{"key":"2406_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106080","volume":"122","author":"J Ba","year":"2023","unstructured":"Ba J, Wang PX, Yang XB, Yu HL, Yu DJ (2023) GLEE: A granularity filter for feature selection. Eng Appl Artif Intell 122:106080. https:\/\/doi.org\/10.1016\/j.engappai.2023.106080","journal-title":"Eng Appl Artif Intell"},{"issue":"7","key":"2406_CR49","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TNNLS.2017.2710422","volume":"29","author":"CZ Wang","year":"2018","unstructured":"Wang CZ, Hu QH, Wang XZ, Chen DG, Qian YH, Dong Z (2018) Feature selection based on neighborhood discrimination index. IEEE Transactions on Neural Networks and Learning Systems 29(7):2986\u20132999. https:\/\/doi.org\/10.1109\/TNNLS.2017.2710422","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2406_CR50","doi-asserted-by":"publisher","unstructured":"Hu QH, Pedrycz W, Yu DR, Lang J (2010) Selecting discrete and continuous features based on neighborhood decision error minimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, 1 https:\/\/doi.org\/10.1109\/TSMCB.2009.2024166","DOI":"10.1109\/TSMCB.2009.2024166"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02406-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02406-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02406-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T07:28:47Z","timestamp":1744010927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02406-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2406"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02406-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"15 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}