发布求助
文献互助 智能选刊 最新文献

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)最新文献

筛选
英文 中文
Image clustering based on SIFT-affinity propagation 基于sift亲和传播的图像聚类
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980860
Yanpeng Zhang, Hong Zhang
{"title":"Image clustering based on SIFT-affinity propagation","authors":"Yanpeng Zhang, Hong Zhang","doi":"10.1109/FSKD.2014.6980860","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980860","url":null,"abstract":"Clustering is a hotspot issues in the field of data mining. There is abundant digital image information in the image acquisition equipment, the image database or the Internet. Facing the large scale image information with rich semantics, it is difficult to obtain accurate information as soon as possible. Therefore, it is essential for us to study efficient image clustering algorithms, in which how to measure the correlation among the samples is key issue, and will directly affect the results of clustering. In this paper, a new image clustering function based on SIFT-Affinity propagation is proposed. First, we extract image features based on Scale-invariant feature transform(SIFT), which are changeless to image zooming, translation, and gyration, and partially changeless to brightness changes and affine. Then the modelling of correlation is established and quantitatively analyzed. Finally, we utilize affinity propagation (AP) to fuse different SIFT correlations among images and obtain clustering results. Our method considers all image data as likely clustering center. The examples results prove the effectiveness and superiority of our approach.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122942043","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: 8
Combining grey systems theory and entropy method to evaluate the organizational health 结合灰色系统理论和熵值法对组织健康状况进行评价
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980913
Xiaoqian Liu, Kan Shi, L. Xing
{"title":"Combining grey systems theory and entropy method to evaluate the organizational health","authors":"Xiaoqian Liu, Kan Shi, L. Xing","doi":"10.1109/FSKD.2014.6980913","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980913","url":null,"abstract":"A combined method based on grey relational analysis and entropy method was used to propose an evaluation model for organizational health, in which the entropy method was introduced to determine the weight of each indicator for organizational health, and the grey relational analysis method was introduced to evaluate and rank the degree of health for each organization. Then we used the evaluation model to evaluate the organizational health of eight companies, and the results showed that this model was practical and useful.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128950807","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
Ensemble based data stream mining with recalling and forgetting mechanisms 基于集成的数据流回收与遗忘机制挖掘
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980873
Yanhuang Jiang, Qiangli Zhao, Yutong Lu
{"title":"Ensemble based data stream mining with recalling and forgetting mechanisms","authors":"Yanhuang Jiang, Qiangli Zhao, Yutong Lu","doi":"10.1109/FSKD.2014.6980873","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980873","url":null,"abstract":"Using ensemble of classifiers on sequential chunks of training instances is a popular strategy for data stream mining. Aiming at the limitations of the existing approaches, we introduce recalling and forgetting mechanisms into ensemble based data stream mining, and put forward a new algorithm MAE (Memorizing based Adaptive Ensemble) to mine chunk-based data streams with concept drifts. Ensemble pruning is used as a recalling mechanism to select useful component classifiers for each incoming data chunk. Ebbinghaus forgetting curve is adopted as a forgetting mechanism to evaluate and replace the component classifiers in the memory repository. Experiments have been performed on datasets with different types of concept drifts. Compared with traditional ensemble approaches, the results show that MAE is a good algorithm with high and stable accuracy, less predicting time and moderate training time.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125573130","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: 7
Provenance-based workflow composition with virtual simulation objects technology 基于溯源的工作流合成与虚拟仿真对象技术
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980964
Pavel A. Smirnov, S. Kovalchuk
{"title":"Provenance-based workflow composition with virtual simulation objects technology","authors":"Pavel A. Smirnov, S. Kovalchuk","doi":"10.1109/FSKD.2014.6980964","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980964","url":null,"abstract":"Accumulated provenance data about correctly constructed and successfully executed scientific workflows provides a valuable source of knowledge, which may be used and reused in new scientific experiments' design. In this paper we propose a provenance-based workflow composition approach - the opposite to package-based one, presented earlier. Processing semantic-compliant provenance data makes available automatic search of already executed workflows (or their parts) in order to reuse them for newly designed applications. This helps the user to be aware only about input and output parameters and variants of intermediate process will be found in provenance and offered for choose. The idea was implemented into Virtual Simulation Objects (VSO) [1] toolbox which provides an intelligent problem solving environment for computational experiments design. Experimental use-case showing the proof of approach hypothesis is also presented.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122500151","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 semantic case based web 2.0 tag hierarchy construction framework 一个基于语义用例的web 2.0标签层次结构构建框架
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980905
Yin Zhang, Kening Gao, Bin Zhang, Li Gao
{"title":"A semantic case based web 2.0 tag hierarchy construction framework","authors":"Yin Zhang, Kening Gao, Bin Zhang, Li Gao","doi":"10.1109/FSKD.2014.6980905","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980905","url":null,"abstract":"In contrast to tags with no inter-relation, web users are more familiar with concept systems with hierarchies, which could also be leveraged in various information retrieval applications. The construction of tag hierarchies relies on the analysis of tag semantic characteristics, while such characteristics are evidenced by semantic case objects which are co-related to tags. Based on such an observation, a semantic case based tag hierarchy construction framework is proposed, together with a semantic flow analysis based hierarchy construction algorithm. Experiment results show that the proposed framework provides an effective mechanism for studying and comparing available algorithms, and the semantic flow based hierarchy construction algorithm outperforms the other evaluated methods in various evaluation metrics.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122585423","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: 2
Ensemble learning model for P2P traffic identification P2P流量识别的集成学习模型
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980874
Shengxiong Deng, Jiangtao Luo, Yong Liu, Xiaoping Wang, Junchao Yang
{"title":"Ensemble learning model for P2P traffic identification","authors":"Shengxiong Deng, Jiangtao Luo, Yong Liu, Xiaoping Wang, Junchao Yang","doi":"10.1109/FSKD.2014.6980874","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980874","url":null,"abstract":"P2P traffic identification is an important issue of internet traffic analysis, and machine learning is a viable approach to address it. However, compared to ensemble learning methods, traditional methods and simple machine learning methods appear to be slightly limited in improving performance. In this paper, Random Forests and feature weighted Naive Bayes was integrated to P2P traffic identification. Scores were calculated for each category in the model while the process of prediction. Then, weighted majority voting was used to get the final output. Experiments were conducted to verify the effectiveness and stability of the integrated model, which implements in the programming mode of MapReduce. Results have shown that the model achieved a better overall performance and may provides an alternative way to solve P2P traffic identification problem.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114781726","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: 6
Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy 基于模糊划分熵的抑制模糊c均值聚类算法参数选择
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980811
Jing Li, Jiu-lun Fan
{"title":"Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy","authors":"Jing Li, Jiu-lun Fan","doi":"10.1109/FSKD.2014.6980811","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980811","url":null,"abstract":"Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In this algorithm, the parameter selection is very important on the algorithm performance. Huang proposed a modified S-FCM, named as MS-FCM, to determine the parameter α with type-driven learning. α is updated each iteration and successful used in MRI segmentation. In this paper, we give another method to select the parameter α based on the fuzzy partition entropy. Numerical examples will serve to illustrate the effectiveness of proposed algorithm.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120965685","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: 6
Density-driven fuzzy connectedness for image segmentation 密度驱动的模糊连通性图像分割
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980819
Ma Ru-Ning, Ding Jun-Di
{"title":"Density-driven fuzzy connectedness for image segmentation","authors":"Ma Ru-Ning, Ding Jun-Di","doi":"10.1109/FSKD.2014.6980819","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980819","url":null,"abstract":"In the traditional fuzzy connectedness (FC) method, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness is presented sufficiently. However, the segmentation performance is largely determined by the specified fuzzy affinity; and the FC method generally has disadvantages such as sensitive to the noise, difficult to determine an appropriate threshold in the case of multiple seeds version, etc. While these defects can be overcome by our method, in which the density properties of image elements are taken into account, and each spel can be characterized by a Neighborhood Density Index (NDI). Based on NDI, a novel way to capture the global fuzzy connectedness is proposed, and related algorithms for fuzzy object extraction are presented. In the paper, detailed evaluations and analysis are made about the segmentation results returned by the proposed algorithms and algorithms of the FC method. Extensive experiments and comparisons are conducted to demonstrate the utility of such novel approach.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183720","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
Self-tuning performance of database systems based on fuzzy rules 基于模糊规则的数据库系统自调优性能
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980831
Zhijie Wei, Zuohua Ding, Jue-liang Hu
{"title":"Self-tuning performance of database systems based on fuzzy rules","authors":"Zhijie Wei, Zuohua Ding, Jue-liang Hu","doi":"10.1109/FSKD.2014.6980831","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980831","url":null,"abstract":"Self-tuning performance of the database management system is a challenging and time-consuming work since multiple performance parameters need to be automatically adjusted. In this paper, we present a new self-tuning method based on fuzzy rules. We first automatically extract Automatic Workload Repository (AWR) report from a database system, and then construct fuzzy rules by identifying the relationship among the performance parameters and performance indicators. Finally, we develop an adaption algorithm to reconfigure the performance parameters with the fuzzy rules. Experiments on TPC-C workload show that the proposed method is feasible and effective.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115696974","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: 8
A knowledge base with dependencies 具有依赖性的知识库
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2014-12-11 DOI: 10.1109/FSKD.2014.6980868
Chaohui Zhuang
{"title":"A knowledge base with dependencies","authors":"Chaohui Zhuang","doi":"10.1109/FSKD.2014.6980868","DOIUrl":"https://doi.org/10.1109/FSKD.2014.6980868","url":null,"abstract":"Traditionally, knowledge is defined as justified true belief (JTB). However, Gettier Problem showed that JTB is not sufficient for knowledge. This paper argues that validity of knowledge has to depend on justifications of knowledge. Therefore, justifications of knowledge have to be stored in knowledge base also. Based on Truth Maintenance Systems, this paper presents a novel knowledge representation system, i.e. knowledge bases with dependencies. This paper applies this idea to propositional logic program. Given a logic program, consequences together with their dependencies are computed and recorded. For updating consequences set with dependencies, this paper proposes an algorithm of rule addition and two algorithms of rule deletion. The second algorithm of rule deletion can handle the problem of circular dependency. Knowledge bases with dependencies can provide a new foundation for belief revision and argument.","PeriodicalId":397095,"journal":{"name":"2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127099706","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: 1
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书