2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第5页

Population Monotonicity of Non-deterministic Computable Aggregations 非确定性可计算聚集的总体单调性

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494508

L. Magdalena, D. Gómez, L. Garmendia, J. Montero

引用次数: 0

Bayesian Pruned Random Rule Foams for XAI XAI的贝叶斯修剪随机规则泡沫

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494525

A. K. Panda, B. Kosko

{"title":"Bayesian Pruned Random Rule Foams for XAI","authors":"A. K. Panda, B. Kosko","doi":"10.1109/FUZZ45933.2021.9494525","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494525","url":null,"abstract":"A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717681","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

A Fuzzy Approach to Language Universals for NLP 语言共相的模糊分析

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494516

Adrià Torrens Urrutia, M. Dolores Jiménez-López, Antoni Brosa-Rodríguez

引用次数: 1

The Concept of Granular Representation of the Information Potential of Variables 变量信息势的粒度表示概念

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494582

Adam Kiersztyn, Paweł Karczmarek, Krystyna Kiersztyn, R. Lopucki, S. Grzegórski, W. Pedrycz

{"title":"The Concept of Granular Representation of the Information Potential of Variables","authors":"Adam Kiersztyn, Paweł Karczmarek, Krystyna Kiersztyn, R. Lopucki, S. Grzegórski, W. Pedrycz","doi":"10.1109/FUZZ45933.2021.9494582","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494582","url":null,"abstract":"With the advent of research into Granular Computing, in particular information granules, the way of thinking about data has changed gradually. Researchers and practitioners do not consider only their specific properties, but also try to look at the data in a more general way, closer to the way people think. This kind of knowledge representation is expressed particularly in approaches based on linguistic modeling or fuzzy techniques such as fuzzy clustering, but also newer approaches related to the explanation of how artificial intelligence works on these data (so-called explainable artificial intelligence). Therefore, especially important from the point of view of the methodology of data research is an attempt to understand their potential as information granules. Such a kind of approach to data presentation and analysis may introduce considerations of a higher, more general level of abstraction, while at the same time reliably describing the network of relationships between the data and the observed information granules. In this study, we tackle this topic with particular emphasis on the problem of choosing a predictive model. In a series of numerical experiments based on both artificially generated data, ecological data on changes in bird arrival dates in the context of climate change, and COVID-19 infections data we demonstrate the effectiveness of the proposed approach built with a novel application of information potential granules.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129823","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

A Framework for Analyzing Fairness, Accountability, Transparency and Ethics: A Use-case in Banking Services 分析公平、问责、透明度和道德的框架:银行服务用例

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494481

Ettore Mariotti, J. M. Alonso, R. Confalonieri

引用次数: 6

Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty 将区间2型模糊集集成到深度嵌入聚类中处理不确定性

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494477

Kutay Bölat, T. Kumbasar

{"title":"Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty","authors":"Kutay Bölat, T. Kumbasar","doi":"10.1109/FUZZ45933.2021.9494477","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494477","url":null,"abstract":"Working with unlabeled data carries the burden of uncertainties especially when the data are high-dimensional. Clustering is not an exception in this aspect and it requires special treatment. In this study, we propose to cope with the uncertainties which occur during clustering high-dimensional data with Interval Type-2 (IT2) Fuzzy Sets (FSs) and Deep Learning (DL) methods. Generation of the IT2-FSs is done with different cluster similarity functions parameterized with Interval Valued Parameters (IVPs). These parameters are introduced as the representations of the uncertainty in cluster assignments. As the backbone of the proposed method, Deep Embedding Clustering (DEC) is employed. The resulting IT2 fuzzy clustering inference is integrated into DEC so that both the inference and the training of the proposed model are operational in popular DL frameworks. Therefore, for a straightforward deployment, the constraints on IT2-FSs are redefined by introducing parameterization tricks upon IVPs. The presented comparative results indicate that coping with the uncertainties through IT2-FSs is superior to their baseline type-1 counterparts.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134639287","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

Robust Possibilistic Optimization with Copula Function 具有Copula函数的鲁棒可能性优化

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494572

R. Guillaume, A. Kasperski, P. Zieliński

引用次数: 1

Exploiting the type-1 OWA operator to fuse the ELICIT information 利用type-1 OWA操作符来融合引出信息

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494400

Wen He, Rosa M. Rodríguez, Bapi Dutta, Luis Martínez

{"title":"Exploiting the type-1 OWA operator to fuse the ELICIT information","authors":"Wen He, Rosa M. Rodríguez, Bapi Dutta, Luis Martínez","doi":"10.1109/FUZZ45933.2021.9494400","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494400","url":null,"abstract":"In a group decision making (GDM) problem, the information is fused to obtain a collective result, which helps to choose the best solution/s to the problem. Recently, a new representation model called Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT), which extends the representation of the comparative linguistic expressions (CLEs) to a continuous domain combining the advantages of the hesitant fuzzy linguistic term sets and 2-tuple linguistic representation model has been proposed to model experts' preferences. Due to the need of fusing experts' preferences in GDM processes, it is convenient to have enough flexible aggregation operators for such processes. However, so far, only two aggregation operators have been introduced to aggregate ELICIT information in GDM problems, the fuzzy arithmetic mean operator and the Bonferroni mean operator. Thus, it seems necessary to define new aggregation operators with different features to model wide range of decision-making scenarios. One widely used operator to aggregate preferences in decision making is the OWA operator. The key issue to apply the OWA operator is the reordering process of the arguments. However, the ELICIT information does not have an inherent order because it is represented by a fuzzy number. Therefore, the aim of this contribution is to define the type-1 ELICIT OWA operator by using crisp and fuzzy weights, particularly interval weights, and define a multi-criteria group decision making model which applies the type-1 ELICIT OWA operator to fuse the information. Additionally, an experimental study is introduced to demonstrate the feasibility of the proposed aggregation operator.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133814847","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

XAI Models for Quality of Experience Prediction in Wireless Networks 无线网络体验质量预测的XAI模型

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494509

Alessandro Renda, P. Ducange, G. Gallo, F. Marcelloni

引用次数: 10

Qualitative Bipolar Decision Frameworks Viewed as Pessimistic/Optimistic Utilities 定性双极决策框架被视为悲观/乐观实用程序

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2021-07-11 DOI: 10.1109/FUZZ45933.2021.9494517

Florence Dupin de Saint-Cyr -- Bannay, R. Guillaume

引用次数: 0