Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.
{"title":"Multi-Label Classification: A Novel approach using decision trees for learning Label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC)","authors":"Hamza Lotf, M. Ramdani","doi":"10.1145/3419604.3419763","DOIUrl":"https://doi.org/10.1145/3419604.3419763","url":null,"abstract":"Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132268286","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}
Abdellah El Kamili, A. Tribak, J. Terhzaz, A. Mediavilla
An Ortho-mode transducer (OMT) using Bøifot orthomode junction is presented for obtaining the maximum wave quality in antenna feeds with very wide performance. The proposed junction is based on the Bøifot configurations, having two symmetry planes for keeping the isolation between orthogonal polarizations and the higher order modes control. The designed circuit provides a combined effect: good matching with very significant size reduction, especially in the transversal plane, reducing mass in satellite systems and allowing feeding important antenna arrays. A Ku-band OMT is presented in order to illustrate the advantages of the introduced junction. The design covers the Ku band 10-15 GHz with more than 25 dB return losses and an insertion losses less than 0.2 dB for both polarizations.
{"title":"OMT design based on Boifot Orthomode junctions for satellite communication applications in the Ku Band","authors":"Abdellah El Kamili, A. Tribak, J. Terhzaz, A. Mediavilla","doi":"10.1145/3419604.3419605","DOIUrl":"https://doi.org/10.1145/3419604.3419605","url":null,"abstract":"An Ortho-mode transducer (OMT) using Bøifot orthomode junction is presented for obtaining the maximum wave quality in antenna feeds with very wide performance. The proposed junction is based on the Bøifot configurations, having two symmetry planes for keeping the isolation between orthogonal polarizations and the higher order modes control. The designed circuit provides a combined effect: good matching with very significant size reduction, especially in the transversal plane, reducing mass in satellite systems and allowing feeding important antenna arrays. A Ku-band OMT is presented in order to illustrate the advantages of the introduced junction. The design covers the Ku band 10-15 GHz with more than 25 dB return losses and an insertion losses less than 0.2 dB for both polarizations.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434980","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 Purpose of our paper is to present a lightweight efficient approach for analysing user stories backlog in order to generate a business process model. A review of literature has been conducted to study contributions in the domain of automatic business process extraction from textual requirements. We found that most of interesting approaches analysing user stories use natural language processing techniques for software projects requirements understanding, and none of them target business process modeling automation. The Originality of our contribution is the proposition of a model driven based parsing of user stories backlog and transformations to generate a process model. This work thus contributes with a novel agile iterative methodology augmenting business process design phase with automation assistant transforming user stories textual requirements into a business process model.
{"title":"Business Process Modelling Augmented: Model Driven transformation of User Stories to Processes","authors":"Karim Baïna, M. Hamlaoui, Hibatallah Kabbaj","doi":"10.1145/3419604.3419793","DOIUrl":"https://doi.org/10.1145/3419604.3419793","url":null,"abstract":"The Purpose of our paper is to present a lightweight efficient approach for analysing user stories backlog in order to generate a business process model. A review of literature has been conducted to study contributions in the domain of automatic business process extraction from textual requirements. We found that most of interesting approaches analysing user stories use natural language processing techniques for software projects requirements understanding, and none of them target business process modeling automation. The Originality of our contribution is the proposition of a model driven based parsing of user stories backlog and transformations to generate a process model. This work thus contributes with a novel agile iterative methodology augmenting business process design phase with automation assistant transforming user stories textual requirements into a business process model.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127448574","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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