GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification
Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a member...
Saved in:
| Published in | IEEE transactions on knowledge and data engineering Vol. 34; no. 3; pp. 1231 - 1242 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1041-4347 1558-2191 |
| DOI | 10.1109/TKDE.2020.2997039 |
Cover
| Abstract | Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than <inline-formula><tex-math notation="LaTeX">O(N^2)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-2997039.gif"/> </inline-formula>. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity <inline-formula><tex-math notation="LaTeX">O(N)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-2997039.gif"/> </inline-formula>, is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html . |
|---|---|
| AbstractList | Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than <inline-formula><tex-math notation="LaTeX">O(N^2)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-2997039.gif"/> </inline-formula>. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity <inline-formula><tex-math notation="LaTeX">O(N)</tex-math> <mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-2997039.gif"/> </inline-formula>, is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html . Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than [Formula Omitted]. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity [Formula Omitted], is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html . |
| Author | Wang, Guoyin Xia, Shuyin Zhang, Hao Chen, Zizhong Giem, Elisabeth Li, Wenhua |
| Author_xml | – sequence: 1 givenname: Shuyin orcidid: 0000-0001-5993-9563 surname: Xia fullname: Xia, Shuyin email: xiasy@cqupt.edu.cn organization: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Telecommunications and Posts, Chongqing, China – sequence: 2 givenname: Hao surname: Zhang fullname: Zhang, Hao email: 1025476698@qq.com organization: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Telecommunications and Posts, Chongqing, China – sequence: 3 givenname: Wenhua surname: Li fullname: Li, Wenhua email: 846659545@qq.com organization: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Telecommunications and Posts, Chongqing, China – sequence: 4 givenname: Guoyin surname: Wang fullname: Wang, Guoyin email: wanggy@cqupt.edu.cn organization: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Telecommunications and Posts, Chongqing, China – sequence: 5 givenname: Elisabeth surname: Giem fullname: Giem, Elisabeth email: gieme01@ucr.edu organization: Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA – sequence: 6 givenname: Zizhong surname: Chen fullname: Chen, Zizhong email: zizhongchen@gmail.com organization: Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA |
| BookMark | eNp9kE1PwjAYxxujiYB-AOOliedhn3Zv9TYR0EgwAbx4Wbqtg5KxYtuR-O3dhHjw4Ol5yf__vPz66LzWtUToBsgQgPD71evTeEgJJUPKeUQYP0M9CILYo8DhvM2JD57P_OgS9a3dEkLiKIYe-pg-zhfLB5zguT7ICi90s97gpXQ4qdbaKLfZ4VIbPBG2bRVi79RB4sQ5o7LGSbyQRZM7pWusajyqhLWqVLnoOlfoohSVldenOEDvk_Fq9OzN3qYvo2Tm5ZQz5zFBuQjCUmZ-kbEiBmC5yAmBKKYhEWFeiMCXADGlhLUfQRaFJWWsrWkRSGADdHecuzf6s5HWpVvdmLpdmdKQBiyMQhK2quioyo221sgyzZX7udMZoaoUSNqBTDuQaQcyPYFsnfDHuTdqJ8zXv57bo0dJKX_1nHDuA2PfnmF-Wg |
| CODEN | ITKEEH |
| CitedBy_id | crossref_primary_10_1109_TNNLS_2023_3325199 crossref_primary_10_1016_j_ins_2022_08_066 crossref_primary_10_3233_JIFS_223960 crossref_primary_10_1016_j_ins_2023_119368 crossref_primary_10_1016_j_knosys_2023_110898 crossref_primary_10_1109_TFUZZ_2022_3206508 crossref_primary_10_1145_3659946 crossref_primary_10_1007_s13042_023_01965_9 crossref_primary_10_1109_TNNLS_2023_3248064 crossref_primary_10_3390_math10040553 crossref_primary_10_1109_ACCESS_2021_3061690 crossref_primary_10_1109_TKDE_2024_3525003 crossref_primary_10_1016_j_inffus_2024_102867 crossref_primary_10_1007_s13042_021_01384_8 crossref_primary_10_1109_TKDE_2024_3419184 crossref_primary_10_1016_j_patcog_2024_111115 crossref_primary_10_1016_j_inffus_2024_102486 crossref_primary_10_1109_TBDATA_2023_3342643 crossref_primary_10_1016_j_ijar_2023_108974 crossref_primary_10_1109_TKDE_2023_3237833 crossref_primary_10_1016_j_ijar_2025_109420 crossref_primary_10_1007_s13042_023_01954_y crossref_primary_10_1007_s10462_024_10976_z crossref_primary_10_1007_s13369_024_09147_7 crossref_primary_10_1016_j_eswa_2024_126030 crossref_primary_10_1016_j_ins_2024_121861 crossref_primary_10_1016_j_neunet_2025_107178 crossref_primary_10_1109_TKDE_2022_3222447 crossref_primary_10_1016_j_ins_2024_120858 crossref_primary_10_1109_TKDE_2024_3405489 crossref_primary_10_32604_cmc_2024_049147 crossref_primary_10_1007_s00500_024_09835_4 crossref_primary_10_1016_j_ijar_2023_108966 crossref_primary_10_1109_TKDE_2024_3428485 crossref_primary_10_1007_s10489_023_04657_7 crossref_primary_10_1007_s10489_024_05533_8 crossref_primary_10_1109_TFUZZ_2024_3437367 crossref_primary_10_1016_j_asoc_2025_112716 crossref_primary_10_1016_j_ins_2023_119753 crossref_primary_10_1007_s10489_024_05904_1 crossref_primary_10_1007_s13042_023_01977_5 crossref_primary_10_1016_j_ijar_2022_05_011 crossref_primary_10_1109_TKDE_2022_3181208 crossref_primary_10_3390_e27010094 crossref_primary_10_1016_j_engappai_2023_106080 crossref_primary_10_1016_j_asoc_2024_112664 crossref_primary_10_1016_j_ijar_2024_109271 crossref_primary_10_1109_TKDE_2024_3474728 crossref_primary_10_3390_electronics12204375 crossref_primary_10_1002_cpe_7388 crossref_primary_10_1016_j_ins_2023_119845 crossref_primary_10_1007_s13042_022_01629_0 crossref_primary_10_1016_j_eswa_2024_125714 crossref_primary_10_1016_j_eswa_2024_123778 crossref_primary_10_1016_j_ijar_2024_109354 crossref_primary_10_1016_j_asoc_2024_111450 crossref_primary_10_1007_s10489_022_03696_w crossref_primary_10_1109_TFUZZ_2023_3275635 crossref_primary_10_1016_j_eswa_2023_122965 crossref_primary_10_1109_TSMC_2021_3119119 crossref_primary_10_1007_s10489_024_06134_1 crossref_primary_10_1016_j_eswa_2023_122324 crossref_primary_10_1016_j_ins_2024_121016 crossref_primary_10_1016_j_scs_2021_102764 crossref_primary_10_1007_s12559_024_10400_2 crossref_primary_10_1007_s13042_021_01404_7 crossref_primary_10_1016_j_ins_2023_119698 crossref_primary_10_1109_TFUZZ_2024_3392328 crossref_primary_10_1109_TFUZZ_2024_3397697 crossref_primary_10_1007_s10489_022_03496_2 crossref_primary_10_1016_j_compbiomed_2023_107538 crossref_primary_10_1007_s13042_022_01708_2 crossref_primary_10_1016_j_inffus_2023_01_019 crossref_primary_10_1016_j_ins_2024_121261 crossref_primary_10_1016_j_engappai_2024_109035 crossref_primary_10_1016_j_ins_2022_09_006 crossref_primary_10_1016_j_ins_2024_121661 crossref_primary_10_32604_iasc_2023_037874 crossref_primary_10_1016_j_ins_2024_120731 crossref_primary_10_1016_j_eswa_2024_123313 crossref_primary_10_1007_s10489_022_04445_9 crossref_primary_10_1007_s13042_023_01775_z crossref_primary_10_1016_j_ijar_2024_109210 crossref_primary_10_1007_s10489_024_05809_z crossref_primary_10_1007_s13042_022_01528_4 crossref_primary_10_1016_j_asoc_2025_112880 crossref_primary_10_3390_sym15050996 crossref_primary_10_1016_j_fss_2025_109382 crossref_primary_10_1155_2021_9945840 crossref_primary_10_1016_j_asoc_2024_111303 crossref_primary_10_1109_TNNLS_2022_3175922 crossref_primary_10_1016_j_inffus_2023_101951 crossref_primary_10_1016_j_eswa_2024_124086 crossref_primary_10_1109_TFUZZ_2023_3250639 crossref_primary_10_1109_TKDE_2023_3249475 crossref_primary_10_1016_j_eswa_2024_125323 crossref_primary_10_1109_TPAMI_2024_3400281 crossref_primary_10_1007_s13042_022_01618_3 crossref_primary_10_1007_s13042_022_01633_4 crossref_primary_10_1007_s13042_020_01257_6 crossref_primary_10_1016_j_knosys_2022_109394 crossref_primary_10_1016_j_ijar_2025_109364 crossref_primary_10_1109_TFUZZ_2023_3261908 crossref_primary_10_1109_TNNLS_2023_3300916 crossref_primary_10_1016_j_ijar_2023_03_002 crossref_primary_10_1109_TKDE_2022_3178090 crossref_primary_10_1109_TKDE_2022_3220200 crossref_primary_10_1016_j_ins_2023_01_046 crossref_primary_10_1016_j_ijar_2022_08_005 crossref_primary_10_3390_sym14081652 |
| Cites_doi | 10.1109/TKDE.2014.2320740 10.1109/TFUZZ.2016.2632745 10.1016/j.ins.2019.01.010 10.1016/j.ins.2008.11.020 10.1016/j.ijar.2018.12.013 10.1126/science.1242072 10.1109/91.660805 10.1007/BF02943234 10.1109/TCYB.2016.2636339 10.1109/TKDE.2012.146 10.1109/TFUZZ.2014.2360548 10.1109/TFUZZ.2006.889960 10.1109/TFUZZ.2015.2426204 10.1109/TKDE.2012.234 10.3724/SP.J.1001.2008.00640 10.1109/TCYB.2015.2496425 10.3724/SP.J.1146.2006.01873 10.1016/j.ins.2006.06.003 10.1016/S0165-0114(97)00077-8 10.1109/TKDE.2012.242 10.1016/s0165-0114(03)00021-6 10.1117/12.669023 10.1109/TFUZZ.2016.2574918 10.1109/TFUZZ.2013.2291570 10.1109/TKDE.2008.147 10.1016/j.artint.2010.04.018 10.1109/ACCESS.2018.2841876 10.1109/TFUZZ.2017.2718492 10.1109/TFUZZ.2016.2581186 10.1109/TFUZZ.2017.2670551 10.1109/TFUZZ.2017.2698420 10.1109/TFUZZ.2014.2327993 10.1109/TCYB.2014.2357892 10.1007/978-94-015-7975-9_21 10.1016/j.knosys.2017.01.008 10.1109/TFUZZ.2014.2387877 10.1109/TFUZZ.2017.2647966 10.1109/TFUZZ.2017.2768044 10.1109/TKDE.2018.2873791 10.1007/BF01001956 10.1016/j.knosys.2018.04.023 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/TKDE.2020.2997039 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1558-2191 |
| EndPage | 1242 |
| ExternalDocumentID | 10_1109_TKDE_2020_2997039 9099413 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61806030; 61936001 funderid: 10.13039/501100001809 – fundername: National Key Research and Development Program of China grantid: 2019QY(Y)0301; 2016QY01W0200 funderid: 10.13039/501100012166 – fundername: Natural Science Foundation of Chongqing grantid: cstc2019jcyj-msxmX0485; cstc2019jcyj-cxttX0002 funderid: 10.13039/501100005230 |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c293t-3a29a56feb4db3d8113cac00178260a6cda54e11822035581b76f2338222d5e13 |
| IEDL.DBID | RIE |
| ISSN | 1041-4347 |
| IngestDate | Mon Jun 30 06:07:44 EDT 2025 Thu Apr 24 23:09:07 EDT 2025 Wed Oct 01 02:06:24 EDT 2025 Wed Aug 27 03:00:17 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c293t-3a29a56feb4db3d8113cac00178260a6cda54e11822035581b76f2338222d5e13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5993-9563 |
| PQID | 2625367606 |
| PQPubID | 85438 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_9099413 proquest_journals_2625367606 crossref_primary_10_1109_TKDE_2020_2997039 crossref_citationtrail_10_1109_TKDE_2020_2997039 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-03-01 |
| PublicationDateYYYYMMDD | 2022-03-01 |
| PublicationDate_xml | – month: 03 year: 2022 text: 2022-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on knowledge and data engineering |
| PublicationTitleAbbrev | TKDE |
| PublicationYear | 2022 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref35 ref34 ref15 ref37 ref14 ref36 ref30 Ling (ref31) 2003; 14 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 Hoa (ref12); 96 ref24 ref45 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 Hu (ref13) 2008; 21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Miao (ref23) 1999; 36 |
| References_xml | – ident: ref7 doi: 10.1109/TKDE.2014.2320740 – ident: ref18 doi: 10.1109/TFUZZ.2016.2632745 – ident: ref37 doi: 10.1016/j.ins.2019.01.010 – ident: ref43 doi: 10.1016/j.ins.2008.11.020 – ident: ref32 doi: 10.1016/j.ijar.2018.12.013 – ident: ref28 doi: 10.1126/science.1242072 – ident: ref17 doi: 10.1109/91.660805 – volume: 14 start-page: 770 issue: 4 year: 2003 ident: ref31 article-title: Theory of fuzzy quotient space (methods of fuzzy granular computing) publication-title: J. Softw. – ident: ref34 doi: 10.1007/BF02943234 – volume: 96 start-page: 1541 volume-title: Proc. Int. Conf. Inf. Process. Manage. Uncertainty ident: ref12 article-title: Some efficient algorithms for rough set methods – ident: ref9 doi: 10.1109/TCYB.2016.2636339 – ident: ref19 doi: 10.1109/TKDE.2012.146 – ident: ref41 doi: 10.1109/TFUZZ.2014.2360548 – ident: ref30 doi: 10.1109/TFUZZ.2006.889960 – ident: ref1 doi: 10.1109/TFUZZ.2015.2426204 – ident: ref3 doi: 10.1109/TKDE.2012.234 – ident: ref15 doi: 10.3724/SP.J.1001.2008.00640 – ident: ref4 doi: 10.1109/TCYB.2015.2496425 – ident: ref38 doi: 10.3724/SP.J.1146.2006.01873 – ident: ref25 doi: 10.1016/j.ins.2006.06.003 – ident: ref44 doi: 10.1016/S0165-0114(97)00077-8 – ident: ref21 doi: 10.1109/TKDE.2012.242 – ident: ref16 doi: 10.1016/s0165-0114(03)00021-6 – ident: ref42 doi: 10.1117/12.669023 – volume: 36 start-page: 681 issue: 6 year: 1999 ident: ref23 article-title: A heuristic algorithm for reduction of knowledge publication-title: J. Comput. Res. Develop. – ident: ref33 doi: 10.1109/TFUZZ.2016.2574918 – ident: ref5 doi: 10.1109/TFUZZ.2013.2291570 – volume: 21 start-page: 730 issue: 6 year: 2008 ident: ref13 article-title: Efficient symbolic and numerical attribute reduction with neighborhood rough sets publication-title: Pattern Recognit. Artif. Intell. – ident: ref20 doi: 10.1109/TKDE.2008.147 – ident: ref26 doi: 10.1016/j.artint.2010.04.018 – ident: ref27 doi: 10.1109/ACCESS.2018.2841876 – ident: ref39 doi: 10.1109/TFUZZ.2017.2718492 – ident: ref40 doi: 10.1109/TFUZZ.2016.2581186 – ident: ref2 doi: 10.1109/TFUZZ.2017.2670551 – ident: ref11 doi: 10.1109/TFUZZ.2017.2698420 – ident: ref45 doi: 10.1109/TFUZZ.2014.2327993 – ident: ref22 doi: 10.1109/TCYB.2014.2357892 – ident: ref29 doi: 10.1007/978-94-015-7975-9_21 – ident: ref8 doi: 10.1016/j.knosys.2017.01.008 – ident: ref6 doi: 10.1109/TFUZZ.2014.2387877 – ident: ref14 doi: 10.1109/TFUZZ.2017.2647966 – ident: ref10 doi: 10.1109/TFUZZ.2017.2768044 – ident: ref36 doi: 10.1109/TKDE.2018.2873791 – ident: ref24 doi: 10.1007/BF01001956 – ident: ref35 doi: 10.1016/j.knosys.2018.04.023 |
| SSID | ssj0008781 |
| Score | 2.6653154 |
| Snippet | Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute... Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1231 |
| SubjectTerms | Adaptive algorithms Approximation algorithms Big Data Classification Complexity Datasets Feature extraction fuzzy rough sets granular ball computing Heuristic algorithms neighborhood rough sets Optimization Reduction Rough set models Rough sets Search problems Source code Time complexity |
| Title | GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification |
| URI | https://ieeexplore.ieee.org/document/9099413 https://www.proquest.com/docview/2625367606 |
| Volume | 34 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2191 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008781 issn: 1041-4347 databaseCode: RIE dateStart: 19890101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1NT-MwEB0BJzgAC6y2u7DygdNqUxwnThpuhaUgED0UkBCXyJ-AKC2CdA_765lx0ooFhLhFkS1ZeuPxPHtmHsA21xmG3dai8SLTSZ2Ko44XKhJOGs9TizZC95Cn_ezoIj2-lJdz8HtWC-OcC8lnrk2f4S3fjs2Ersp2CgxnUpKonc87WV2rNfO6nTwIkiK7QE6UpHnzghnzYuf85M8BMkHB2-h70cKL_86gIKryxhOH46W3AqfThdVZJXftSaXb5t-rno2fXfkqLDdxJuvWhvEF5txoDVamGg6s2dJrsPSiIeE6XB3u9Qdnu6zL-uO_bsgGpOHDzlzFusPr8eNtdXPPMMplPfWEv6x6IGfJulUtm-XYgBrBEtTsdsSC3iZlIgXwN-Cid3C-fxQ16guRwRCgihIlCiUz73RqdWI7cZwYZehUQ0bCVWaskqkjfiI49WiPdZ55gYwXIw4rXZx8hYXReOS-Acu9Q8wlR2sxqdeJktrHXhfeikx6mbeAT_EoTdOanBQyhmWgKLwoCcKSICwbCFvwazbloe7L8dHgdYJkNrBBowWbU9DLZuc-lQIJIXWx49n392f9gEVBJRAhD20TFqrHidvCwKTSP4NFPgMjLN0Y |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1NbxMxEB1V7QE4tNCCCBTwgRNiU6_X3s1yC9AQaJNDmkoVl5U_adU0qdpND_x6ZrybiC8hbquVLVl64_E8e2YewGtucgy7nUPjRaYjvU6TXhA6EV7ZwKVDG6F7yNE4H57KL2fqbAPermthvPcx-cx36TO-5buFXdJV2UGJ4YwkidotJaVUTbXW2u_2iihJivwCWVEmi_YNM-XlwfTo4yFyQcG76H3RxstfTqEoq_KHL44HzGAHRqulNXkll91lbbr2-29dG_937Q9hu400Wb8xjUew4ee7sLNScWDtpt6FBz-1JNyDr5_ejycn71ifjRd3fsYmpOLDTnzN-rNvi5uL-vyKYZzLBvoWfzl9Te6S9etGOMuzCbWCJbDZxZxFxU3KRYrwP4bTweH0wzBp9RcSi0FAnWRalFrlwRvpTOZ6aZpZbelcQ07CdW6dVtITQxGcurSnpsiDQM6LMYdTPs2ewOZ8MfdPgRXBI-qKo71YGUymlQlpMGVwIldBFR3gKzwq2zYnJ42MWRVJCi8rgrAiCKsWwg68WU-5bjpz_GvwHkGyHtii0YH9FehVu3dvK4GUkPrY8fzZ32e9gnvD6ei4Ov48PnoO9wUVRMSstH3YrG-W_gWGKbV5Ga3zB-Dk4GU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=GBNRS%3A+A+Novel+Rough+Set+Algorithm+for+Fast+Adaptive+Attribute+Reduction+in+Classification&rft.jtitle=IEEE+transactions+on+knowledge+and+data+engineering&rft.au=Xia%2C+Shuyin&rft.au=Zhang%2C+Hao&rft.au=Li%2C+Wenhua&rft.au=Wang%2C+Guoyin&rft.date=2022-03-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1041-4347&rft.eissn=1558-2191&rft.volume=34&rft.issue=3&rft.spage=1231&rft_id=info:doi/10.1109%2FTKDE.2020.2997039&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1041-4347&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1041-4347&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1041-4347&client=summon |