2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第9页

Feature and decision level fusion using multiple kernel learning and fuzzy integrals 特征和决策级融合使用多核学习和模糊积分

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337934

Anthony J. Pinar, T. Havens, Derek T. Anderson, Lequn Hu

{"title":"Feature and decision level fusion using multiple kernel learning and fuzzy integrals","authors":"Anthony J. Pinar, T. Havens, Derek T. Anderson, Lequn Hu","doi":"10.1109/FUZZ-IEEE.2015.7337934","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337934","url":null,"abstract":"Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used to learn the aggregation of a set of valid kernels into a single (ideally) superior kernel. The aggregation can be done using weighted sums of the pre-computed kernels, but determining the summation weights is not a trivial task. A popular and successful approach to this problem is MKL-group lasso (MKLGL), where the weights and classification surface are simultaneously solved by iteratively optimizing a min-max optimization until convergence. In this work, we propose an ℓp-normed genetic algorithm MKL (GAMKLp), which uses a genetic algorithm to learn the weights of a set of pre-computed kernel matrices for use with MKL classification. We prove that this approach is equivalent to a previously proposed fuzzy integral aggregation of multiple kernels called fuzzy integral: genetic algorithm (FIGA). A second algorithm, which we call decision-level fuzzy integral MKL (DeFIMKL), is also proposed, where a fuzzy measure with respect to the fuzzy Choquet integral is learned via quadratic programming, and the decision value-viz., the class label-is computed using the fuzzy Choquet integral aggregation. Experiments on several benchmark data sets show that our proposed algorithms can outperform MKLGL when applied to support vector machine (SVM)-based classification.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127600961","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: 16

Acquisition of human operation characteristics for kite-based tethered flying robot using human operation data 基于人操作数据的风筝系绳飞行机器人人操作特征获取

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337942

Chiaki Todoroki, Yasutake Takahashi, Takayuki Nakamura

引用次数: 0

A Hierarchical perspective to Fuzzy Inductive Reasoning 模糊归纳推理的层次视角

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338067

Solmaz Bagherpour, F. Mugica, À. Nebot

引用次数: 1

The prametric-based GDM selection procedure under linguistic assessments 语言评估下基于参数的GDM选择程序

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337821

Fujun Hou

{"title":"The prametric-based GDM selection procedure under linguistic assessments","authors":"Fujun Hou","doi":"10.1109/FUZZ-IEEE.2015.7337821","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337821","url":null,"abstract":"The prametric-based procedure is a group decision making (GDM) selection process minimizing a consensus gap indicator, which is not a metric but a prametric. The prametric is an `almost metric' which does not necessarily satisfy the triangle inequality but able to describe the consensus intransitivity in GDM. This paper considers the procedure under a linguistic situation, where the individuals preferences are provided as linguistic preference relations. The procedure contains two main stages. The first stage looks for the individual's ties-permitted ordinal rankings from the individual's opinions. In order to do this, we introduce an acceptable consistency criterion for linguistic preference relations and show some related properties. If the linguistic preference relation is acceptable, we then obtain the ties-permitted ordinal ranking directly. Otherwise, the ties-permitted ordinal ranking will be deduced by minimizing a consensus gap. The second stage looks for the final solution sets of alternatives by minimizing the gap between a potential solution and the rankings obtained in the first stage. Some illustrative examples are included.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121480396","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: 13

Incremental RBF network models for nonlinear approximation and classification 增量RBF网络模型的非线性逼近与分类

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338093

G. Vachkov, V. Stoyanov, N. Christova

{"title":"Incremental RBF network models for nonlinear approximation and classification","authors":"G. Vachkov, V. Stoyanov, N. Christova","doi":"10.1109/FUZZ-IEEE.2015.7338093","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338093","url":null,"abstract":"In this paper a multistep learning algorithm for creating a novel incremental Radial Basis Function Network (RBFN) Model is presented and analyzed. The proposed incremental RBFN model has a composite structure that consists of one initial linear sub-model and a number of incremental sub-models, each of them being able to gradually decrease the overall approximation error of the model, until a desired accuracy is achieved. The identification of the entire incremental RBFN model is divided into a series of identifications steps applied to smaller size sub-models. At each identification step the Particle Swarm Optimization algorithm (PSO) with constraints is used to optimize the small number of parameters of the respective sub-model. A synthetic nonlinear test example is used in the paper to analyze the performance of the proposed multistep learning algorithm for the incremental RBFN model. A real wine quality data set is also used to illustrate the usage of the proposed incremental model for solving nonlinear classification problems. A brief comparison with the classical single RBFN model with large number of parameters is conducted in the paper and shows the merits of the incremental RBFN model in terms of efficiency and accuracy.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122641152","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

Design of Matlab/Simulink based development board for fuzzy logic education 基于Matlab/Simulink的模糊逻辑教学开发板的设计

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337840

A. Albayrak, Muammer Albayrak, R. Bayir

引用次数: 6

A novel clustering algorithm based on a new similarity measure over Intuitionistic fuzzy sets 一种基于直觉模糊集相似性测度的聚类算法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337946

Rinki Solanki, Q. Lohani, Pranab K. Muhuri

{"title":"A novel clustering algorithm based on a new similarity measure over Intuitionistic fuzzy sets","authors":"Rinki Solanki, Q. Lohani, Pranab K. Muhuri","doi":"10.1109/FUZZ-IEEE.2015.7337946","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337946","url":null,"abstract":"In Intuitionistic fuzzy sets(IFSs), experts assign both membership value and non-membership value to each fuzzy element x with a certain degree of hesitation. The hesitancy in the opinion of the experts appear due to incomplete information available regarding x. Therefore, precise estimation of its both membership value and non-membership value becomes highly difficult. Hence, there is a high chance that both membership value and the non-membership value assigned to x by the expert may not be absolutely correct. So, whenever we try to measure similarity between the IFSs using the various distance measures involving all the components of IFSs like membership value, non-membership value together with hesitation, then we often notice that all of them fails to describe the underlying situation completely. Therefore, the similarity measures derived from these distance measures also fails to produce good results. So, we introduce a new similarity measure by properly defining a similarity degree through the result established in this paper. The similarity measure has a central role in developing a modified λ-cutting algorithm for clustering. Here we also establish the efficacy of our modified λ-cutting algorithm while implementing it on a real world data set.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123297521","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: 19

Which bio-diversity indices are most adequate 哪些生物多样性指数是最合适的

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337831

O. Kosheleva, C. Tweedie, V. Kreinovich

引用次数: 2

Cuts or thresholds, what is the best reduction method in fuzzy formal concept analysis? 切量或阈值，模糊形式概念分析中的最佳约简方法是什么?

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337990

M. E. Cornejo, J. Medina, Eloísa Ramírez-Poussa

引用次数: 2

Effect of different initializations on EKM algorithm 不同初始化对EKM算法的影响

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337810

Syed Moshfeq Salaken, A. Khosravi, S. Nahavandi, Dongrui Wu

引用次数: 3