2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)最新文献

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LDCnet: Minimizing the cost of supervision for various types of concept drift LDCnet:最小化各种概念漂移的监督成本
Piotr Sobolewski, Michal Wozniak
{"title":"LDCnet: Minimizing the cost of supervision for various types of concept drift","authors":"Piotr Sobolewski, Michal Wozniak","doi":"10.1109/CIDUE.2013.6595774","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595774","url":null,"abstract":"Supervision cost is often overlooked when designing decision systems to cope with concept drift. The solution presented in this article utilizes low supervision while achieving similar efficiency to the state-of-the-art methods. Algorithm bases on a net of classification models which cover the whole feature space, preparing the system for every possible concept. The experiments are performed in the simulated environment with four scenarios representing different types of concept drift.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134102531","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}
引用次数: 3
An incremental approach for updating approximations of rough fuzzy set under the variation of attribute values 属性值变化下粗糙模糊集逼近的增量更新方法
Anping Zeng, Tianrui Li, Chuan Luo, Junbo Zhang
{"title":"An incremental approach for updating approximations of rough fuzzy set under the variation of attribute values","authors":"Anping Zeng, Tianrui Li, Chuan Luo, Junbo Zhang","doi":"10.1109/CIDUE.2013.6595772","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595772","url":null,"abstract":"Rough Set Theory (RST) is a powerful mathematical tool for dealing with inconsistent information in decision situations. In real-life applications, information systems in RST often vary with time. Approximations of a concept in RST have been used to induce rules and need to update for dynamic data mining and related tasks. In addition, the values of the decision attributes in information systems may be fuzzy. An extension of classical rough set model, rough fuzzy set, is then presented to deal with such values. This paper focuses on approaches for dynamically updating approximations in rough fuzzy set when attribute values are coarsened or refined. The principles for dynamic maintenance of upper and lower approximations are firstly presented. Then, the algorithms are developed for updating approximations incrementally under the variation of attributes' values. Some examples are employed to illustrate the proposed methods. A comparison of the proposed incremental method with a non-incremental method for dynamic maintenance of approximations is conducted by an extensive experimental evaluation on the data set from UCI. The experimental results show that the incremental method effectively reduce the computing time in comparison with the non-incremental method.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133028603","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}
引用次数: 3
Similarity-based evolution control for fitness estimation in particle swarm optimization 基于相似性的粒子群优化适应度估计进化控制
Chaoli Sun, J. Zeng, Jeng-Shyang Pan, Yaochu Jin
{"title":"Similarity-based evolution control for fitness estimation in particle swarm optimization","authors":"Chaoli Sun, J. Zeng, Jeng-Shyang Pan, Yaochu Jin","doi":"10.1109/CIDUE.2013.6595765","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595765","url":null,"abstract":"Evolution control in the surrogate-assisted evolutionary and other meta-heuristic optimization algorithms is essential for their success in efficiently achieving the global optimum. In order to further reduce the number of fitness evaluations, a similarity-based evolution control method is introduced into the fitness estimation strategy for particle swarm optimization (FESPSO) [1]. In the proposed method, the fitness of a particle is either estimated or evaluated, depending on its similarity to the particle whose fitness is known. The performance of the proposed algorithm is examined on eight benchmark problems, and the simulation results show that the proposed algorithm is highly competitive on reducing the number of required fitness evaluations using the computationally expensive fitness function.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129287972","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}
引用次数: 12
Issues with performance measures for dynamic multi-objective optimisation 动态多目标优化的性能度量问题
Mardé Helbig, A. Engelbrecht
{"title":"Issues with performance measures for dynamic multi-objective optimisation","authors":"Mardé Helbig, A. Engelbrecht","doi":"10.1109/CIDUE.2013.6595767","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595767","url":null,"abstract":"In recent years a number of algorithms were proposed to solve dynamic multi-objective optimisation problems. However, a major problem in the field of dynamic multi-objective optimisation is a lack of standard performance measures to quantify the quality of solutions found by an algorithm. In addition, the selection of performance measures may lead to misleading results. This paper highlights issues that may cause misleading results when comparing dynamic multi-objective optimisation algorithms with performance measures that are currently used in the field.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116512222","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}
引用次数: 33
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