$O(N^2)$

O(N2)

. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity

$O(N)$

O(N)

, 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.","publishedDate","2020-05-25","publishedYear",2020,"venue","IEEE Transactions on Knowledge and Data Engineering",230,"authors","keywords",[153,154],"pdfUrl","topVenue","Rough Sets and Fuzzy Logic","Data Mining Algorithms and Applications","paper_W2908436867","Granular ball computing classifiers for efficient, scalable and robust learning","{\"openalex\":\"W2908436867\",\"doi\":\"10.1016/j.ins.2019.01.010\"}","2026-05-22T03:21:52.257159293","2026-05-22T03:21:52.257161903","10.1016/j.ins.2019.01.010","W2908436867","2019-01-06",2019,"Information Sciences",266,[153,167,168],"Neural Networks and Applications","Advanced Computational Techniques and Applications",{"_60":170,"_62":171},[],[172],{"_65":173,"_67":174,"_69":175,"_71":176},1785283216502,{"_74":178,"_76":77,"_78":173,"_79":-5,"_80":26,"_81":26,"_82":26,"_83":-5,"_84":-5,"_85":86,"_35":87,"_88":89},[73,177],"[\"portal\",\"industries\"]","industries",[179,180,181,182,183,184,185,186,187,188,189,190],{"_15":252,"_29":253,"_31":-5,"_32":254,"_33":255,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":256,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":253,"_59":255},{"_15":248,"_29":249,"_31":-5,"_32":-5,"_33":250,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":251,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":249,"_59":250},{"_15":242,"_29":243,"_31":-5,"_32":244,"_33":245,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":246,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":243,"_59":245},{"_15":238,"_29":239,"_31":-5,"_32":-5,"_33":240,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":241,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":239,"_59":240},{"_15":234,"_29":235,"_31":-5,"_32":-5,"_33":236,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":237,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":235,"_59":236},{"_15":228,"_29":229,"_31":-5,"_32":230,"_33":231,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":232,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":229,"_59":231},{"_15":223,"_29":224,"_31":-5,"_32":-5,"_33":225,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":226,"_203":26,"_204":227,"_205":26,"_206":-5,"_58":224,"_59":225},{"_15":219,"_29":220,"_31":-5,"_32":-5,"_33":221,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":222,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":220,"_59":221},{"_15":215,"_29":216,"_31":-5,"_32":-5,"_33":217,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":218,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":216,"_59":217},{"_15":211,"_29":212,"_31":-5,"_32":-5,"_33":213,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":214,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":212,"_59":213},{"_15":207,"_29":208,"_31":-5,"_32":-5,"_33":209,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":210,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":208,"_59":209},{"_15":191,"_29":192,"_31":-5,"_32":-5,"_33":193,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":194,"_42":-5,"_44":-5,"_195":196,"_197":-5,"_198":77,"_199":200,"_201":202,"_203":26,"_204":86,"_205":26,"_206":-5,"_58":192,"_59":193},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[233],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[247],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[257,258],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_261":24},"SITE_BASE_URL"]

Shuyin Xia 人物

Shuyin Xia · 相关文章