2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第2页

Intrusion detection system by fuzzy interpolation 采用模糊插值的入侵检测系统

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015710

Longzhi Yang, Jie Li, Gerhard Fehringer, P. Barraclough, G. Sexton, Yi Cao

引用次数: 26

A comparison of fuzzy approaches for training a humanoid robotic football player 训练仿人机器人足球运动员的模糊方法比较

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015756

G. Acampora, A. D. Nuovo, B. Siciliano, A. Vitiello

引用次数: 0

IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods 爱荷华和交叉比例均匀算子作为情感分析和集成方法中的聚合工具

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-24 DOI: 10.1109/FUZZ-IEEE.2017.8015375

Orestes Appel, F. Chiclana, Jenny Carter, H. Fujita

{"title":"IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods","authors":"Orestes Appel, F. Chiclana, Jenny Carter, H. Fujita","doi":"10.1109/FUZZ-IEEE.2017.8015375","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015375","url":null,"abstract":"In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decision-making problem, which in turn translates into two sub-problems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874859","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

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-23 DOI: 10.1109/FUZZ-IEEE.2017.8015440

Changhong Fu, Andriy Sarabakha, E. Kayacan, Christian Wagner, R. John, J. Garibaldi

引用次数: 6

A method for improving the generation of linguistic summaries 一种改进语言摘要生成的方法

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-23 DOI: 10.1109/FUZZ-IEEE.2017.8015752

A. Wilbik, U. Kaymak, R. Dijkman

引用次数: 6

Modeling patients' methylmalonic acid levels using probabilistic fuzzy systems 用概率模糊系统模拟患者的甲基丙二酸水平

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-08-23 DOI: 10.1109/FUZZ-IEEE.2017.8015766

Rui Jorge Almeida, Saskia van Loon, U. Kaymak, A. Wilbik, V. Scharnhorst, A. Boer

{"title":"Modeling patients' methylmalonic acid levels using probabilistic fuzzy systems","authors":"Rui Jorge Almeida, Saskia van Loon, U. Kaymak, A. Wilbik, V. Scharnhorst, A. Boer","doi":"10.1109/FUZZ-IEEE.2017.8015766","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015766","url":null,"abstract":"Vitamin B12 deficiency is a common disorder with severe impacts on hematological and neurological disorders. Identifying vitamin B12 deficiency is not straightforward since blood vitamin B12 levels are not representative for actual vitamin B12 status in tissue. Instead, methylmalonic acid (MMA) levels in the plasma are used as indicators of vitamin B12 deficiency. MMA concentrations increase starting from the early course of vitamin B12 deficiency but they may also be high regardless of vitamin B12 deficiency due to renal failure (measured by eGFR). In this paper we propose the use of probabilistic fuzzy systems (PFS) to explore the relationship between MMA plasma levels with vitamin B12 and kidney function. We propose a PFS model for the analysis of overall MMA properties for all patients and also specific MMA properties for individual patients. We show that this PFS model leads to accurate MMA interval predictions. We further show that the proposed model can be used to assess a change in the eGFR level to a normal eGFR level, and its effect on the patient's MMA distribution.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124554303","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

Exact fuzzy k-nearest neighbor classification for big datasets 大数据集的精确模糊k近邻分类

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-10 DOI: 10.1109/FUZZ-IEEE.2017.8015686

Jesús Maillo, J. Luengo, S. García, F. Herrera, I. Triguero

{"title":"Exact fuzzy k-nearest neighbor classification for big datasets","authors":"Jesús Maillo, J. Luengo, S. García, F. Herrera, I. Triguero","doi":"10.1109/FUZZ-IEEE.2017.8015686","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015686","url":null,"abstract":"The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to classify. There are several approaches to enhance its precision, with the Fuzzy k-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. Fuzzy-kNN computes a fuzzy degree of membership of each instance to the classes of the problem. As a result, it generates smoother borders between classes. Apart from the existing kNN approach to handle big datasets, there is not a fuzzy variant to manage that volume of data. Nevertheless, calculating this class membership adds an extra computational cost becoming even less scalable to tackle large datasets because of memory needs and high runtime. In this work, we present an exact and distributed approach to run the Fuzzy-kNN classifier on big datasets based on Spark, which provides the same precision than the original algorithm. It presents two separately stages. The first stage transforms the training set adding the class membership degrees. The second stage classifies with the kNN algorithm the test set using the class membership computed previously. In our experiments, we study the scaling-up capabilities of the proposed approach with datasets up to 11 million instances, showing promising results.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121796820","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: 20

Knowledge-based consistency index for fuzzy pairwise comparison matrices 基于知识的模糊两两比较矩阵一致性指标

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-10 DOI: 10.1109/FUZZ-IEEE.2017.8015380

S. Kubler, W. Derigent, A. Voisin, J. Robert, Yves Le Traon

{"title":"Knowledge-based consistency index for fuzzy pairwise comparison matrices","authors":"S. Kubler, W. Derigent, A. Voisin, J. Robert, Yves Le Traon","doi":"10.1109/FUZZ-IEEE.2017.8015380","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015380","url":null,"abstract":"Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert's time in some MCDM problems.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134080766","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

How arithmetically fuzzy are we? An empirical comparison of human imprecise calculation and fuzzy arithmetic 我们在算术上有多模糊?人的不精确计算与模糊算法的实证比较

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015629

S. Lefort, Marie-Jeanne Lesot, E. Zibetti, C. Tijus, Marcin Detyniecki

引用次数: 1

Hybrid multi-objective forecasting of solar photovoltaic output using Kalman filter based interval type-2 fuzzy logic system 基于卡尔曼滤波的区间2型模糊逻辑系统混合多目标预测太阳能光伏输出

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015733

Saima Hassan, M. A. Khanesar, A. Hajizadeh, A. Khosravi

{"title":"Hybrid multi-objective forecasting of solar photovoltaic output using Kalman filter based interval type-2 fuzzy logic system","authors":"Saima Hassan, M. A. Khanesar, A. Hajizadeh, A. Khosravi","doi":"10.1109/FUZZ-IEEE.2017.8015733","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015733","url":null,"abstract":"Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA/D) in the second hybrid algorithm. Root mean square error and maximum absolute error as the two accuracy objective are utilized to find the Pareto-optimal solution with the MOPSO and MOEA/D respectively. The proposed hybrid multi-objective designs of the interval type-2 fuzzy logic system are utilized to the prediction of solar photovoltaic output. It is observed that MOEA/D outperforms MOPSO in this case in terms of quality of results and its diversity. Finally, one point is selected from the obtained Pareto front and its performance is illustrated.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129248865","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