M. Moradi, M. Rahmanimanesh, A. Shahzadi, R. Monsefi
{"title":"Smooth unsupervised domain adaptation considering uncertainties","authors":"M. Moradi, M. Rahmanimanesh, A. Shahzadi, R. Monsefi","doi":"10.2139/ssrn.4331056","DOIUrl":"https://doi.org/10.2139/ssrn.4331056","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81218453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The profit maximization set covering problem is a general model able to formulate practical problems including in particular an application in the mining industry. As a variant of the partial set covering problem, the studied problem is to select some subsets of elements to maximize the difference of the total gain of the covered elements subtracting the costs of the chosen subsets and their associated groups. We investigate for the first time a learning-based multi-start iterated local search algorithm for solving the problem. The proposed algorithm combines a multi-restart mechanism to enhance robustness, an intensification-driven local search to perform intensive solution examination, a learning-driven initialization to obtain high-quality starting solutions and a learning-based strategy to select suitable perturbations. Experimental results on 30 benchmark instances show the competitiveness of the algorithm against the state-of-the-art methods, by reporting 18 new lower bounds and 12 equal results (including 7 known optimal results). We also perform additional experiments to validate the design of the algorithmic components.
{"title":"Learning-based multi-start iterated local search for the profit maximization set covering problem","authors":"Wen Sun, Wenlong Li, Jin-Kao Hao, Qinghua Wu","doi":"10.2139/ssrn.4349053","DOIUrl":"https://doi.org/10.2139/ssrn.4349053","url":null,"abstract":"The profit maximization set covering problem is a general model able to formulate practical problems including in particular an application in the mining industry. As a variant of the partial set covering problem, the studied problem is to select some subsets of elements to maximize the difference of the total gain of the covered elements subtracting the costs of the chosen subsets and their associated groups. We investigate for the first time a learning-based multi-start iterated local search algorithm for solving the problem. The proposed algorithm combines a multi-restart mechanism to enhance robustness, an intensification-driven local search to perform intensive solution examination, a learning-driven initialization to obtain high-quality starting solutions and a learning-based strategy to select suitable perturbations. Experimental results on 30 benchmark instances show the competitiveness of the algorithm against the state-of-the-art methods, by reporting 18 new lower bounds and 12 equal results (including 7 known optimal results). We also perform additional experiments to validate the design of the algorithmic components.","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74144043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongqiang Zheng, Jie Ding, Feng Liu, Dongqing Wang
{"title":"Adaptive neural decision tree for EEG based emotion recognition","authors":"Yongqiang Zheng, Jie Ding, Feng Liu, Dongqing Wang","doi":"10.2139/ssrn.4331057","DOIUrl":"https://doi.org/10.2139/ssrn.4331057","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81919718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Managing consensus in balanced networks based on opinion and Trust/Distrust evolutions","authors":"Quanbo Zha, Xi He, Min Zhan, Ning Lang","doi":"10.2139/ssrn.4390549","DOIUrl":"https://doi.org/10.2139/ssrn.4390549","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84996636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A two-stage deep graph clustering method for identifying the evolutionary patterns of the time series of animation view counts","authors":"Duokui He, Zhongjun Tang, Qianqian Chen, Zhong-Xing Han, Dongyuan Zhao, Fengxia Sun","doi":"10.2139/ssrn.4331041","DOIUrl":"https://doi.org/10.2139/ssrn.4331041","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75635095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alfredo Ibias, Varun Varma, Karol Capała, L. Gherardini, Jose Sousa
{"title":"SaNDA: A small and iNcomplete dataset analyser","authors":"Alfredo Ibias, Varun Varma, Karol Capała, L. Gherardini, Jose Sousa","doi":"10.2139/ssrn.4364273","DOIUrl":"https://doi.org/10.2139/ssrn.4364273","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87454793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tracking and handling behavioral biases in active learning frameworks","authors":"D. Agarwal, Balasubramaniam Natarajan","doi":"10.2139/ssrn.4293710","DOIUrl":"https://doi.org/10.2139/ssrn.4293710","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89865976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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