2013 IEEE International Conference on Granular Computing (GrC)最新文献_第7页

A mapping between fuzzy covering information systems 模糊覆盖信息系统之间的映射

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740428

Changzhong Wang, Wenying Bao, Xiuxiu Wang, Qiang He

引用次数: 1

(k, ε)-Anonymity: An anonymity model for thwarting similarity attack (k， ε)-匿名:一种阻止相似攻击的匿名模型

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740431

Haiyuan Wang, Jianmin Han, Jiyi Wang, Lixia Wang

引用次数: 7

Forecasting model based on an improved Elman neural network and its application in the agricultural production 改进的Elman神经网络预测模型及其在农业生产中的应用

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GRC.2013.6740408

Liu Yi, Xu Ke, Song Junde, Zhao Yuwen, Bi Qiang

引用次数: 1

Modeling and control of a sugars precipitation process for Chinese medicine mixed solution 中药混合溶液糖沉淀过程的建模与控制

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740385

Hongjun Duan, Qingwei Li

引用次数: 0

The construction of general type-2 fuzzy sets 一般2型模糊集的构造

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740396

D. Hu, T. Lin, Qiang Fan

引用次数: 1

An error analysis on locally linear embedding 局部线性嵌入的误差分析

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740451

Peng Zhang, Chunbo Fan, Yuanyuan Ren, Zhou Sun

引用次数: 0

Multi-objective opposition-based learning fully informed particle swarm optimizer with favour ranking 基于多目标对立学习的全信息粒子群优化算法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740391

Ying Gao, Lingxi Peng, Fufang Li, Miao Liu, Waixi Liu

{"title":"Multi-objective opposition-based learning fully informed particle swarm optimizer with favour ranking","authors":"Ying Gao, Lingxi Peng, Fufang Li, Miao Liu, Waixi Liu","doi":"10.1109/GrC.2013.6740391","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740391","url":null,"abstract":"Some particle swarm optimization(PSO) algorithms have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. In this paper, we propose a new opposition-based learning fully informed particle swarm optimizer with favour ranking to solve multi-objective optimization problems. Instead of Pareto dominance, favour ranking is used to identify the best individuals in order to guide the search process in the proposed algorithm. Different from other multi-objective PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning and favour ranking. Besides, all personal best positions are considered to update particle position. Because of discarding the particle velocity and using full information and favour ranking, the algorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. Convergence metric, diversity metric are used to evaluate the performance of the algorithm. The experimental results show that the algorithm is effective on the benchmark functions.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115513965","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

A secure query method for EPCGlobal networks EPCGlobal网络的安全查询方法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740389

Ge Fu, Xinran Liu, Hong Zhang

引用次数: 0

Measurement and analysis of Shanghai Xinmin microblog website 上海新民微博网站的测量与分析

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740432

Jian Wang, Xiaolin Xu, Xiaoming Hu, Yinchun Yang, Xiumei Wu

引用次数: 0

Integrating multiple information of active learning for image classification 集成多种主动学习信息进行图像分类

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740439

Haihui Xu, Pengpeng Zhao, Jian Wu, Zhiming Cui, Chengchao Li

{"title":"Integrating multiple information of active learning for image classification","authors":"Haihui Xu, Pengpeng Zhao, Jian Wu, Zhiming Cui, Chengchao Li","doi":"10.1109/GrC.2013.6740439","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740439","url":null,"abstract":"In the application of image classification, active learning algorithm can effectively alleviate the efforts of labeling by selecting the most informative instances for user annotation, as well as obtain a satisfactory classifier. Traditional active learning methods do not consider the cost of manual labeling, which is usually regarded as the same. They focus on minimizing the classification error, aiming at improving the classifier performance. However, in fact, the user annotation cost is not equal and changes dynamically. We introduce the value of the information framework to measure the instance informativeness, which including misclassification risk and the cost of user annotation. While the value of information is based on probability over the current classifier, only taking into the labeled examples account, thus it may query the outliers. In order to simultaneously lever the distribution information of a large amount of the remaining unlabeled instances, we use information density to measure the representativeness of the sample. To this end, we propose an integrating multiple information of active learning method for image classification (IMIM), which incorporates the strength of both value of information and information density measure criteria by a heuristic weighting strategy. At last, select the most informative instance by the expected error reduction method. Compared with the state of art method, experimental results on diverse datasets demonstrate the effectiveness of our proposed method.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127391313","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: 0