Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494578
Jocines D. F. Silveira, Tiago Rocha Martins, Cristiano Neri da Silva, J. V. D. Reis
This paper proposes a Fuzzy system to assist in the decision making of the deployment plan for the Internet of Things (IoT) communication infrastructure for effective exchange of information between devices (sensors, actuators, controllers, among others) in the Smart Farming scenario. The system offers great potential to assist managers to choose the implementation between the LoRaWAN, LoRaMesh or hybrid technologies, as well reflect on service quality, reduction of implantation costs, sensing and performance of devices in the rural scenario. These technologies were implemented in a real scenario in order to obtain the basis for the rules of the proposed Fuzzy system. The scenario adopted for data validation is a rural area of 162 ha located at the Center of Agricultural Sciences (CCA) of the Federal University of Piauí (UFPI), Teresina, Piauí, Brazil. In which assess the performance of technologies and obtain parameters for the Fuzzy system, data were obtained regarding the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and the packet loss rate. This resulted in a Fuzzy system capable of recommending among one of the technologies mentioned, helping in the choice of the most appropriate communication infrastructure for a given Smart Farming scenario.
{"title":"New Solution based on Fuzzy System for Planning IoT Communication Infrastructure for Rural Areas","authors":"Jocines D. F. Silveira, Tiago Rocha Martins, Cristiano Neri da Silva, J. V. D. Reis","doi":"10.1109/FUZZ45933.2021.9494578","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494578","url":null,"abstract":"This paper proposes a Fuzzy system to assist in the decision making of the deployment plan for the Internet of Things (IoT) communication infrastructure for effective exchange of information between devices (sensors, actuators, controllers, among others) in the Smart Farming scenario. The system offers great potential to assist managers to choose the implementation between the LoRaWAN, LoRaMesh or hybrid technologies, as well reflect on service quality, reduction of implantation costs, sensing and performance of devices in the rural scenario. These technologies were implemented in a real scenario in order to obtain the basis for the rules of the proposed Fuzzy system. The scenario adopted for data validation is a rural area of 162 ha located at the Center of Agricultural Sciences (CCA) of the Federal University of Piauí (UFPI), Teresina, Piauí, Brazil. In which assess the performance of technologies and obtain parameters for the Fuzzy system, data were obtained regarding the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and the packet loss rate. This resulted in a Fuzzy system capable of recommending among one of the technologies mentioned, helping in the choice of the most appropriate communication infrastructure for a given Smart Farming scenario.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"9 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":"125907237","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/FUZZ45933.2021.9494525
A. K. Panda, B. Kosko
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.
{"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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494395
Clément Iphar, L. Boudet, Jean-Philippe Poli
Fuzzy logic has been successfully used in various crisis management systems. In such systems, the geographical aspect is usually very important and relies on Geographical Information Systems. Most of the approaches are focused on 2D information. In this paper, we use the fuzzy morpho-mathematics framework to define new relations to reason on the topography with a digital terrain model. In particular, we focus on the characterisation of the line of greatest dip. Without loss of generality, we then illustrate those relations on a case of runoff from a building and a terrain.
{"title":"Topography-based Fuzzy Assessment of Runoff Area with 3D Spatial Relations","authors":"Clément Iphar, L. Boudet, Jean-Philippe Poli","doi":"10.1109/FUZZ45933.2021.9494395","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494395","url":null,"abstract":"Fuzzy logic has been successfully used in various crisis management systems. In such systems, the geographical aspect is usually very important and relies on Geographical Information Systems. Most of the approaches are focused on 2D information. In this paper, we use the fuzzy morpho-mathematics framework to define new relations to reason on the topography with a digital terrain model. In particular, we focus on the characterisation of the line of greatest dip. Without loss of generality, we then illustrate those relations on a case of runoff from a building and a terrain.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"37 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":"123019624","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/FUZZ45933.2021.9494452
A. C. V. Pinto, Petrônio C. L. Silva, F. Guimarães, Christian Wagner, E. Aguiar
Time series forecasting is an essential research field that provides significant data to help professionals in several areas. Thus, growing research and development in this area have been conducted, aiming at developing new forecasting methods with higher performance levels, but always also with low processing costs. One of this methods is Fuzzy Time Series - FTS. However, one great problem of FTS prediction is how to properly deal with the uncertainty associated to the time series and to model's design. Thus, in this paper we propose a univariate interval type-2 fuzzy time series model combined with the concept of Self-organised Direction Aware Data Partitioning Algorithm (SODA) for universe of discourse partitioning. All experiments were performed using the TAIEX data set and the results were then compared to other forecasting models from literature. A sliding window methodology was applied and the forecast error metric chosen was the Root Mean Squared Error (RMSE) for all methods. SODA-T2FTS results show that it outperformed other forecasting methods confirming that interval type-2 fuzzy logic can be a reliable tool for time series prediction.
{"title":"Self-Organised Direction Aware Data Partitioning for Type-2 Fuzzy Time Series Prediction","authors":"A. C. V. Pinto, Petrônio C. L. Silva, F. Guimarães, Christian Wagner, E. Aguiar","doi":"10.1109/FUZZ45933.2021.9494452","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494452","url":null,"abstract":"Time series forecasting is an essential research field that provides significant data to help professionals in several areas. Thus, growing research and development in this area have been conducted, aiming at developing new forecasting methods with higher performance levels, but always also with low processing costs. One of this methods is Fuzzy Time Series - FTS. However, one great problem of FTS prediction is how to properly deal with the uncertainty associated to the time series and to model's design. Thus, in this paper we propose a univariate interval type-2 fuzzy time series model combined with the concept of Self-organised Direction Aware Data Partitioning Algorithm (SODA) for universe of discourse partitioning. All experiments were performed using the TAIEX data set and the results were then compared to other forecasting models from literature. A sliding window methodology was applied and the forecast error metric chosen was the Root Mean Squared Error (RMSE) for all methods. SODA-T2FTS results show that it outperformed other forecasting methods confirming that interval type-2 fuzzy logic can be a reliable tool for time series prediction.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"52 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":"126583026","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/FUZZ45933.2021.9494462
A. Buck, Derek T. Anderson, James M. Keller, R. Luke, G. Scott
Three dimensional point cloud data sets are easy to acquire and manipulate, but are often too large to process directly for embedded real-time applications. The spatial information in a point cloud can be represented in a variety of reduced forms, such as voxel grids, Gaussian mixture models, or spatial semantic structures. In this article, we show how a segmented point cloud can be represented as a spatial relationship graph using bounding boxes and triangular fuzzy numbers. This model is a lightweight encoding of the relative distance and direction between objects, and can be used to describe and query for particular spatial configurations using linguistic terms in a multicriteria framework. We show how this approach can be applied on a hand-segmented subset of the NPM3D data set with several illustrative examples. The work herein has useful applications in many applied domains, such as human-robot interaction with unmanned aerial systems.
{"title":"A Fuzzy Spatial Relationship Graph for Point Clouds Using Bounding Boxes","authors":"A. Buck, Derek T. Anderson, James M. Keller, R. Luke, G. Scott","doi":"10.1109/FUZZ45933.2021.9494462","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494462","url":null,"abstract":"Three dimensional point cloud data sets are easy to acquire and manipulate, but are often too large to process directly for embedded real-time applications. The spatial information in a point cloud can be represented in a variety of reduced forms, such as voxel grids, Gaussian mixture models, or spatial semantic structures. In this article, we show how a segmented point cloud can be represented as a spatial relationship graph using bounding boxes and triangular fuzzy numbers. This model is a lightweight encoding of the relative distance and direction between objects, and can be used to describe and query for particular spatial configurations using linguistic terms in a multicriteria framework. We show how this approach can be applied on a hand-segmented subset of the NPM3D data set with several illustrative examples. The work herein has useful applications in many applied domains, such as human-robot interaction with unmanned aerial systems.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"53 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":"126586179","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/FUZZ45933.2021.9494601
Yifan Wang, H. Ishibuchi, Jihua Zhu, Yaxiong Wang, Tao Dai
Fuzzy systems have proven to be an effective tool for classification and regression. However, they have been mainly applied to supervised tasks. In this paper, we extend fuzzy systems to tackle unsupervised problems based on the manifold regularization framework and convolution/pooling technologies. The proposed fuzzy system, referred to as the unsupervised fuzzy neural network, can extract features from raw images accurately and perform well on image clustering. The main structure of the proposed approach is divided into three parts: fuzzy mapping, unsupervised feature extraction and manifold representation. We adopt K-means to perform clustering in the low-dimensional manifold space. Experimental results on image datasets demonstrate that our approach is competitive with classical and state-of-the-art algorithms. We also identify the relative contributions of each component of the proposed approach in experiments.
{"title":"Unsupervised Fuzzy Neural Network for Image Clustering","authors":"Yifan Wang, H. Ishibuchi, Jihua Zhu, Yaxiong Wang, Tao Dai","doi":"10.1109/FUZZ45933.2021.9494601","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494601","url":null,"abstract":"Fuzzy systems have proven to be an effective tool for classification and regression. However, they have been mainly applied to supervised tasks. In this paper, we extend fuzzy systems to tackle unsupervised problems based on the manifold regularization framework and convolution/pooling technologies. The proposed fuzzy system, referred to as the unsupervised fuzzy neural network, can extract features from raw images accurately and perform well on image clustering. The main structure of the proposed approach is divided into three parts: fuzzy mapping, unsupervised feature extraction and manifold representation. We adopt K-means to perform clustering in the low-dimensional manifold space. Experimental results on image datasets demonstrate that our approach is competitive with classical and state-of-the-art algorithms. We also identify the relative contributions of each component of the proposed approach in experiments.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"23 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":"126594517","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/FUZZ45933.2021.9494561
Lijie Han, M. Song, W. Pedrycz
In this paper, we propose a new approach to solve linguistic group decision making (GDM) problems through defining different linguistic terms for each expert and optimizing those terms. Information granules are often designed as the framework of linguistic terms and to vividly describe the approach, intervals are selected to express linguistic terms as large, medium, and small in the paper. Analytic Hierarchy Process (AHP) is set as the basic model and abstracted as linguistic reciprocal matrices. The abstraction process is carefully designed considering two strategies: each expert owns same linguistic terms (same distribution of cutting-points in an interval) and each expert owns different linguistic terms. As comparison, three methods of cutting-points allocation for the two strategies are realized with a synthetic example: optimizing allocation, uniform allocation and random allocation. The results coincide with theoretical analysis: each expert owns different linguistic terms reach the highest consensus.
{"title":"An Approach to Determine Best Cutting-points in Group Decision Making Problems with Information Granules","authors":"Lijie Han, M. Song, W. Pedrycz","doi":"10.1109/FUZZ45933.2021.9494561","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494561","url":null,"abstract":"In this paper, we propose a new approach to solve linguistic group decision making (GDM) problems through defining different linguistic terms for each expert and optimizing those terms. Information granules are often designed as the framework of linguistic terms and to vividly describe the approach, intervals are selected to express linguistic terms as large, medium, and small in the paper. Analytic Hierarchy Process (AHP) is set as the basic model and abstracted as linguistic reciprocal matrices. The abstraction process is carefully designed considering two strategies: each expert owns same linguistic terms (same distribution of cutting-points in an interval) and each expert owns different linguistic terms. As comparison, three methods of cutting-points allocation for the two strategies are realized with a synthetic example: optimizing allocation, uniform allocation and random allocation. The results coincide with theoretical analysis: each expert owns different linguistic terms reach the highest consensus.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 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":"126497278","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/FUZZ45933.2021.9494512
Gabriella Casalino, G. Castellano, Katarzyna Kaczmarek-Majer, O. Hryniewicz
Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to process such multiple data streams with a low percentage of labelling. Furthermore, both acoustic data and psychiatric labels are subject to several sources of uncertainty (e.g., irregular phone usage, background noises, subjectivity in psychiatric evaluation). To cope with these characteristics of an acoustic data stream, this paper introduces an intelligent qualitative and quantitative analysis based on the Dynamic Incremental Semi-Supervised Fuzzy C-Means algorithm (DISSFCM) for supporting bipolar disorder monitoring. The proposed approach is illustrated with real-life data collected from smartphones and psychiatric assessments of a bipolar disorder patient. Analysis of the dynamics of data streams basing on the cluster prototypes from fuzzy semi-supervised learning is a highly novel approach. It is also showed that the DISSFCM algorithm obtains relatively high classification performance (accuracy ranging from 0.66 to 0.76) already with 25% labelling percentage, thanks to the splitting mechanism that is adapting the number of clusters to the structure of data.
{"title":"Intelligent analysis of data streams about phone calls for bipolar disorder monitoring","authors":"Gabriella Casalino, G. Castellano, Katarzyna Kaczmarek-Majer, O. Hryniewicz","doi":"10.1109/FUZZ45933.2021.9494512","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494512","url":null,"abstract":"Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to process such multiple data streams with a low percentage of labelling. Furthermore, both acoustic data and psychiatric labels are subject to several sources of uncertainty (e.g., irregular phone usage, background noises, subjectivity in psychiatric evaluation). To cope with these characteristics of an acoustic data stream, this paper introduces an intelligent qualitative and quantitative analysis based on the Dynamic Incremental Semi-Supervised Fuzzy C-Means algorithm (DISSFCM) for supporting bipolar disorder monitoring. The proposed approach is illustrated with real-life data collected from smartphones and psychiatric assessments of a bipolar disorder patient. Analysis of the dynamics of data streams basing on the cluster prototypes from fuzzy semi-supervised learning is a highly novel approach. It is also showed that the DISSFCM algorithm obtains relatively high classification performance (accuracy ranging from 0.66 to 0.76) already with 25% labelling percentage, thanks to the splitting mechanism that is adapting the number of clusters to the structure of data.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 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":"130128330","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/FUZZ45933.2021.9494509
Alessandro Renda, P. Ducange, G. Gallo, F. Marcelloni
Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.
{"title":"XAI Models for Quality of Experience Prediction in Wireless Networks","authors":"Alessandro Renda, P. Ducange, G. Gallo, F. Marcelloni","doi":"10.1109/FUZZ45933.2021.9494509","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494509","url":null,"abstract":"Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955348","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/FUZZ45933.2021.9494517
Florence Dupin de Saint-Cyr -- Bannay, R. Guillaume
A bipolar structure called BLF expresses knowledge about decisions in terms of decision principles that are ranked and polarized according to the utility of the consequences of these decisions. A BLF allows us to compare decisions under incomplete knowledge. For a given decision, the BLF returns a vector of utility/dis-utility in terms of achievement of positive/negative goals. Decisions are compared thanks to these vectors. In this paper we focus on the link between the uncertain knowledge aggregation made by the BLF and classical aggregation functions used in decision under uncertainty and multi-criteria approaches. The main benefit of a BLF is that thanks to the bipolar scale, positive and negative goals can be dealt with independently under their own point of view (each of them being either pessimistic or optimistic).
{"title":"Qualitative Bipolar Decision Frameworks Viewed as Pessimistic/Optimistic Utilities","authors":"Florence Dupin de Saint-Cyr -- Bannay, R. Guillaume","doi":"10.1109/FUZZ45933.2021.9494517","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494517","url":null,"abstract":"A bipolar structure called BLF expresses knowledge about decisions in terms of decision principles that are ranked and polarized according to the utility of the consequences of these decisions. A BLF allows us to compare decisions under incomplete knowledge. For a given decision, the BLF returns a vector of utility/dis-utility in terms of achievement of positive/negative goals. Decisions are compared thanks to these vectors. In this paper we focus on the link between the uncertain knowledge aggregation made by the BLF and classical aggregation functions used in decision under uncertainty and multi-criteria approaches. The main benefit of a BLF is that thanks to the bipolar scale, positive and negative goals can be dealt with independently under their own point of view (each of them being either pessimistic or optimistic).","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":"122604822","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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