{"title":"Fuzzy Systems and Data Mining VIII - Proceedings of FSDM 2022, Virtual Event, 4-7 November 2022","authors":"","doi":"10.3233/faia358","DOIUrl":"https://doi.org/10.3233/faia358","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129128359","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}
{"title":"Control Design for One Class of Uncertain Metzler-Takagi-Sugeno Time-Delay Systems","authors":"D. Krokavec, A. Filasová","doi":"10.3233/FAIA210195","DOIUrl":"https://doi.org/10.3233/FAIA210195","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485326","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}
. A sampling method is one of the popular methods to deal with an imbalance problem appearing in machine learning. A dataset having an imbalance problem contains a noticeably different number of instances belonging to different classes. Three sampling techniques are used to solve this problem by balancing class distributions. The first one is an undersampling technique removing noises from a class having a large number of instances, called a majority class. The second one is an over-sampling technique synthesizing instances from a class having a small number of instances, called a minority class, and the third one is the combined technique of both undersampling and oversampling. This research applies the combined technique of both undersampling and oversampling via the mass ratio variance scores of instances from each individual class. For the majority class, instances with high mass ratio variances are removed whereas for the minority class, instances with high mass ratio variances are used in synthesizing minority instances. The results of this proposed sampling technique help improve recall over standard classifiers: a decision tree, a random forest, Linear SVM, MLP on all synthesized datasets; however it may have low precision. So the combined measure of precision and recall is used, F1-score. Recall and F1-scores of synthesized datasets and UCI datasets are significantly better for collections of datasets having small imbalance ratio. Moreover, the Wilcoxon signed-rank test is used to confirm the improvement for datasets having imbalance ratio smaller than or equal to 0.2.
{"title":"Mass Ratio Variance Majority Undersampling and Minority Oversampling Technique for Class Imbalance","authors":"Piboon Polvimoltham, K. Sinapiromsaran","doi":"10.3233/FAIA210186","DOIUrl":"https://doi.org/10.3233/FAIA210186","url":null,"abstract":". A sampling method is one of the popular methods to deal with an imbalance problem appearing in machine learning. A dataset having an imbalance problem contains a noticeably different number of instances belonging to different classes. Three sampling techniques are used to solve this problem by balancing class distributions. The first one is an undersampling technique removing noises from a class having a large number of instances, called a majority class. The second one is an over-sampling technique synthesizing instances from a class having a small number of instances, called a minority class, and the third one is the combined technique of both undersampling and oversampling. This research applies the combined technique of both undersampling and oversampling via the mass ratio variance scores of instances from each individual class. For the majority class, instances with high mass ratio variances are removed whereas for the minority class, instances with high mass ratio variances are used in synthesizing minority instances. The results of this proposed sampling technique help improve recall over standard classifiers: a decision tree, a random forest, Linear SVM, MLP on all synthesized datasets; however it may have low precision. So the combined measure of precision and recall is used, F1-score. Recall and F1-scores of synthesized datasets and UCI datasets are significantly better for collections of datasets having small imbalance ratio. Moreover, the Wilcoxon signed-rank test is used to confirm the improvement for datasets having imbalance ratio smaller than or equal to 0.2.","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127955901","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}
Xie Hu, Huikun Pei, Bingcai Liu, Chen Wang, C. Hao
{"title":"Real Time Warning Model of Transmission Tower Tilt Based on Multi-Sensor Data","authors":"Xie Hu, Huikun Pei, Bingcai Liu, Chen Wang, C. Hao","doi":"10.3233/FAIA210220","DOIUrl":"https://doi.org/10.3233/FAIA210220","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124462967","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}
{"title":"Fuzzy Systems and Data Mining VII - Proceedings of FSDM 2021, Virtual Event, 26-29 October 2021","authors":"","doi":"10.3233/faia340","DOIUrl":"https://doi.org/10.3233/faia340","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229815","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}
{"title":"Social Media User Profiling Based on Genre Extraction","authors":"K. Belousov, I. Labutin","doi":"10.3233/FAIA210207","DOIUrl":"https://doi.org/10.3233/FAIA210207","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127578735","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}
{"title":"Responses of Climate Indicators to Droughts in SF Bay Area","authors":"Patrick Li, Gang Li","doi":"10.3233/FAIA210206","DOIUrl":"https://doi.org/10.3233/FAIA210206","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243229","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}
{"title":"Multi-Step Low-Rank Decomposition of Large PageRank Matrices","authors":"Zhao-Li Shen, B. Carpentieri","doi":"10.3233/FAIA210212","DOIUrl":"https://doi.org/10.3233/FAIA210212","url":null,"abstract":"","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"1192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132060179","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}
Akira Fujisawa, Kazuyuki Matsumoto, Kazuki Ohta, Minoru Yoshida, K. Kita
In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also used VGG16, ResNet-50, Inception v3, and Xception’s pre-trained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy rate of 80% or more was obtained in the “human” category.
{"title":"ASCII Art Classification Model by Transfer Learning and Data Augmentation","authors":"Akira Fujisawa, Kazuyuki Matsumoto, Kazuki Ohta, Minoru Yoshida, K. Kita","doi":"10.3233/faia200738","DOIUrl":"https://doi.org/10.3233/faia200738","url":null,"abstract":"In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also used VGG16, ResNet-50, Inception v3, and Xception’s pre-trained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy rate of 80% or more was obtained in the “human” category.","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121032673","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}
The generalized interval observer design conditions for continuous-time Metzlerian Takagi-Sugeno systems are presented in the paper. Attention is focused on the analysis and design guaranteeing the asymptotic convergence of the interval observer error and positivity of interval observer state. The relationship between the nonnegativity of the observer gains and the corresponding positive observer state attractiveness is also shown. The method presented extends and generalizes the results that recently appeared in the literature.
{"title":"Interval Observer Design for Metzlerian Takagi-Sugeno Systems","authors":"D. Krokavec, A. Filasová","doi":"10.3233/faia200736","DOIUrl":"https://doi.org/10.3233/faia200736","url":null,"abstract":"The generalized interval observer design conditions for continuous-time Metzlerian Takagi-Sugeno systems are presented in the paper. Attention is focused on the analysis and design guaranteeing the asymptotic convergence of the interval observer error and positivity of interval observer state. The relationship between the nonnegativity of the observer gains and the corresponding positive observer state attractiveness is also shown. The method presented extends and generalizes the results that recently appeared in the literature.","PeriodicalId":128279,"journal":{"name":"Fuzzy Systems and Data Mining","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122585265","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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