Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494589
Jean-Philippe Poli, W. Ouerdane, Régis Pierrard
Semantic image annotation is a field of paramount importance in which deep learning excels. However, some application domains, like security or medicine, may need an explanation of this annotation. Explainable Artificial Intelligence is an answer to this need. In this work, an explanation is a sentence in natural language that is dedicated to human users to provide them clues about the process that leads to the decision: the labels assignment to image parts. We focus on semantic image annotation with fuzzy logic that has proven to be a useful framework that captures both image segmentation imprecision and the vagueness of human spatial knowledge and vocabulary. In this paper, we present an algorithm for textual explanation generation of the semantic annotation of image regions.
{"title":"Generation of Textual Explanations in XAI: the Case of Semantic Annotation","authors":"Jean-Philippe Poli, W. Ouerdane, Régis Pierrard","doi":"10.1109/FUZZ45933.2021.9494589","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494589","url":null,"abstract":"Semantic image annotation is a field of paramount importance in which deep learning excels. However, some application domains, like security or medicine, may need an explanation of this annotation. Explainable Artificial Intelligence is an answer to this need. In this work, an explanation is a sentence in natural language that is dedicated to human users to provide them clues about the process that leads to the decision: the labels assignment to image parts. We focus on semantic image annotation with fuzzy logic that has proven to be a useful framework that captures both image segmentation imprecision and the vagueness of human spatial knowledge and vocabulary. In this paper, we present an algorithm for textual explanation generation of the semantic annotation of image regions.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"25 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":"127261196","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494584
Aykut Beke, T. Kumbasar
In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression. Within this paper, we construct two interval FLSs that have a different representation of uncertainty. One of them models the uncertainty in its consequents while the other one within its antecedents that are defined with interval type-2 Fuzzy Sets (FSs). The learning approach uses a multi-objective composite loss that is formed by the mean square error for accuracy purposes along with tilted loss for enforcing the bounds of the FLSs to capture the expected amount of uncertainty. In that way, it is not only possible to learn the FLSs that represent the uncertainty within their MFs (which can be used as prediction intervals) but also to improve the regression performance since the composite loss provides a more complete representation of the data. We present the proposed learning approach alongside parameterization tricks so that they can be trained within the frameworks of deep learning while not violating the definitions of FSs. We present comparative results on benchmark datasets that have different characteristics.
{"title":"Capturing Uncertainty with Interval Fuzzy Logic Systems through Composite Deep Learning","authors":"Aykut Beke, T. Kumbasar","doi":"10.1109/FUZZ45933.2021.9494584","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494584","url":null,"abstract":"In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression. Within this paper, we construct two interval FLSs that have a different representation of uncertainty. One of them models the uncertainty in its consequents while the other one within its antecedents that are defined with interval type-2 Fuzzy Sets (FSs). The learning approach uses a multi-objective composite loss that is formed by the mean square error for accuracy purposes along with tilted loss for enforcing the bounds of the FLSs to capture the expected amount of uncertainty. In that way, it is not only possible to learn the FLSs that represent the uncertainty within their MFs (which can be used as prediction intervals) but also to improve the regression performance since the composite loss provides a more complete representation of the data. We present the proposed learning approach alongside parameterization tricks so that they can be trained within the frameworks of deep learning while not violating the definitions of FSs. We present comparative results on benchmark datasets that have different characteristics.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"0 550 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":"130446288","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}
Pub Date : 2021-07-11DOI: 10.1109/fuzz45933.2021.9494532
{"title":"[Copyright notice]","authors":"","doi":"10.1109/fuzz45933.2021.9494532","DOIUrl":"https://doi.org/10.1109/fuzz45933.2021.9494532","url":null,"abstract":"","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":"128994453","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494455
K. Rudnik, A. Chwastyk, I. Pisz, G. Bocewicz
This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In addition, it is the basis for the development of research on the inference method with ordered fuzzy rules, which may become an indispensable tool for decision-making in an uncertain environment.
{"title":"Ordered fuzzy rules generation based on incremental dataset","authors":"K. Rudnik, A. Chwastyk, I. Pisz, G. Bocewicz","doi":"10.1109/FUZZ45933.2021.9494455","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494455","url":null,"abstract":"This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In addition, it is the basis for the development of research on the inference method with ordered fuzzy rules, which may become an indispensable tool for decision-making in an uncertain environment.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"19 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":"127832066","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494439
Rafael R. C. Silva, W. Caminhas, P. C. de Lima e Silva, F. Guimarães
In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.
{"title":"A C4.5 Fuzzy Decision Tree Method for Multivariate Time Series Forecasting","authors":"Rafael R. C. Silva, W. Caminhas, P. C. de Lima e Silva, F. Guimarães","doi":"10.1109/FUZZ45933.2021.9494439","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494439","url":null,"abstract":"In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"11 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":"117008228","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494421
Pedro Messias, Maria João Sousa, Alexandra Moutinho
Image-based fire detection is a safety-critical task, which requires high-quality datasets to ensure performance guarantees in real scenarios. Automatic fire detection systems are in ever-increasing demand, but the limited number and size of open datasets, and lack of annotations, hinder model development. Solving this issue requires that experts dedicate a significant time to classify and segment fire events in image datasets. Towards building large-scale curated datasets, this paper presents a data annotation method that leverages semantic segmentation based on superpixel aggregation and color features. The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts. The performance of the proposed algorithm is evaluated for relevant scenarios using a publicly available dataset, namely through the assessment of the segmentation quality and the labeling of fire color categories. The outcomes of this approach pave the way for creating large-scale datasets that can empower future deployments of learning-based architectures in fire detection systems.
{"title":"Color-based Superpixel Semantic Segmentation for Fire Data Annotation","authors":"Pedro Messias, Maria João Sousa, Alexandra Moutinho","doi":"10.1109/FUZZ45933.2021.9494421","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494421","url":null,"abstract":"Image-based fire detection is a safety-critical task, which requires high-quality datasets to ensure performance guarantees in real scenarios. Automatic fire detection systems are in ever-increasing demand, but the limited number and size of open datasets, and lack of annotations, hinder model development. Solving this issue requires that experts dedicate a significant time to classify and segment fire events in image datasets. Towards building large-scale curated datasets, this paper presents a data annotation method that leverages semantic segmentation based on superpixel aggregation and color features. The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts. The performance of the proposed algorithm is evaluated for relevant scenarios using a publicly available dataset, namely through the assessment of the segmentation quality and the labeling of fire color categories. The outcomes of this approach pave the way for creating large-scale datasets that can empower future deployments of learning-based architectures in fire detection systems.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860116","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494557
D. Sharma, Prashant K. Gupta, Javier Andreu-Perez, J. Mendel, Luis Martínez-López
Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.
{"title":"A Python Software Library for Computing with Words and Perceptions","authors":"D. Sharma, Prashant K. Gupta, Javier Andreu-Perez, J. Mendel, Luis Martínez-López","doi":"10.1109/FUZZ45933.2021.9494557","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494557","url":null,"abstract":"Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"57 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":"124520619","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494547
S. Aguzzoli, P. Codara
The algebraic semantics of Gödel propositional logic is given by the variety of Gödel algebras, which in turns form a category dually equivalent to the pro-finite completion of the category of finite forests and order-preserving open maps. Forests provide a sound and complete semantics for propositional infinite-valued Gödel logic, while propositional k-valued Gödel logic is sound and complete for forests of height at most $k-1$. In this work we shall mainly deal with three-valued Gödel logic. We shall show that the subcategory of forests of height at most 2 (bushes) forms an elementary topos, thus providing naturally a generalisation to bushes of all classical first-order set concepts, suitable for developing a first-order three-valued Gödel logic semantics based on bush concepts instead of sets.
{"title":"Towards an Algebraic Topos Semantics for Three-valued Gödel Logic","authors":"S. Aguzzoli, P. Codara","doi":"10.1109/FUZZ45933.2021.9494547","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494547","url":null,"abstract":"The algebraic semantics of Gödel propositional logic is given by the variety of Gödel algebras, which in turns form a category dually equivalent to the pro-finite completion of the category of finite forests and order-preserving open maps. Forests provide a sound and complete semantics for propositional infinite-valued Gödel logic, while propositional k-valued Gödel logic is sound and complete for forests of height at most $k-1$. In this work we shall mainly deal with three-valued Gödel logic. We shall show that the subcategory of forests of height at most 2 (bushes) forms an elementary topos, thus providing naturally a generalisation to bushes of all classical first-order set concepts, suitable for developing a first-order three-valued Gödel logic semantics based on bush concepts instead of sets.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"203 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":"124545260","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494483
Francisco J. Rodríguez-Lozano, J. C. Gámez-Granados, O. Baños, J. Alcalá-Fdez, J. M. Soto-Hidalgo
Internet of Things enables sensors and actuators to share heterogeneous data between different devices. Such data can be used to create intelligent systems to control diverse structures available in houses, cities, or industrial environments among others. In this context, one of the most used approaches to handle these intelligent systems is based on Fuzzy Rule-Based Systems (FRBS) due to their suitability for addressing complex data and managing their imprecision. However, most of the current developments in this area are usually ad-hoc solutions limited by the intercommunication between FRBS and IoT devices. This results into significant challenges in reusing these solutions to solve latent problems. To bridge this gap, a new module for the open source library JFML is proposed to offer a complete implementation of an IoT infrastructure to develop intelligent IoT solutions based on the IEEE std 1855–2016. Moreover, a case study with real IoT devices is presented to showcase the use of the proposed module.
{"title":"An approach to bridge the gap between ubiquitous embedded devices and JFML: A new module for Internet of Things","authors":"Francisco J. Rodríguez-Lozano, J. C. Gámez-Granados, O. Baños, J. Alcalá-Fdez, J. M. Soto-Hidalgo","doi":"10.1109/FUZZ45933.2021.9494483","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494483","url":null,"abstract":"Internet of Things enables sensors and actuators to share heterogeneous data between different devices. Such data can be used to create intelligent systems to control diverse structures available in houses, cities, or industrial environments among others. In this context, one of the most used approaches to handle these intelligent systems is based on Fuzzy Rule-Based Systems (FRBS) due to their suitability for addressing complex data and managing their imprecision. However, most of the current developments in this area are usually ad-hoc solutions limited by the intercommunication between FRBS and IoT devices. This results into significant challenges in reusing these solutions to solve latent problems. To bridge this gap, a new module for the open source library JFML is proposed to offer a complete implementation of an IoT infrastructure to develop intelligent IoT solutions based on the IEEE std 1855–2016. Moreover, a case study with real IoT devices is presented to showcase the use of the proposed module.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"68 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":"124553755","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494595
Barbara Pekala, Krzysztof Dyczkowski, Jaroslaw Szkola, Dawid Kosior
The article deals with the problem of selecting the most appropriate attributes for a given classification method with the use of inclusion and similarity measures for interval-valued fuzzy sets. These types of measures with uncertainty were introduced using partial or linear order. The article introduces a modified IV-Relief algorithm using the above-mentioned measures. The theoretical considerations were supported by the analysis of the effectiveness of the proposed algorithm on a well-known dataset on breast cancer diagnostics. The proposed methods make it possible to extend the recognized classification methods so that they operate on uncertain data.
{"title":"Classification of uncertain data with a selection of relevant features based on similarities measures of Interval-Valued Fuzzy Sets","authors":"Barbara Pekala, Krzysztof Dyczkowski, Jaroslaw Szkola, Dawid Kosior","doi":"10.1109/FUZZ45933.2021.9494595","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494595","url":null,"abstract":"The article deals with the problem of selecting the most appropriate attributes for a given classification method with the use of inclusion and similarity measures for interval-valued fuzzy sets. These types of measures with uncertainty were introduced using partial or linear order. The article introduces a modified IV-Relief algorithm using the above-mentioned measures. The theoretical considerations were supported by the analysis of the effectiveness of the proposed algorithm on a well-known dataset on breast cancer diagnostics. The proposed methods make it possible to extend the recognized classification methods so that they operate on uncertain data.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"234 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":"124574294","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}
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