Loza Mencía, Eneldo ; Fürnkranz, Johannes ; Hüllermeier, Eyke ; Rapp, Michael
Hrsg.: Escalante, Hugo Jair ; Escalera, Sergio ; Guyon, Isabelle ; Baró, Xavier ; Gerven, Marcel van ; Güclütürk, Yagmur ; Güclü, Umut (2018)
Learning Interpretable Rules for Multi-Label Classification.
In: Explainable and Interpretable Models in Computer Vision and Machine Learning
doi: 10.1007/978-3-319-98131-4_4
Buchkapitel, Bibliographie
Kurzbeschreibung (Abstract)
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.
| Typ des Eintrags: | Buchkapitel |
|---|---|
| Erschienen: | 2018 |
| Herausgeber: | Escalante, Hugo Jair ; Escalera, Sergio ; Guyon, Isabelle ; Baró, Xavier ; Gerven, Marcel van ; Güclütürk, Yagmur ; Güclü, Umut |
| Autor(en): | Loza Mencía, Eneldo ; Fürnkranz, Johannes ; Hüllermeier, Eyke ; Rapp, Michael |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning Interpretable Rules for Multi-Label Classification |
| Sprache: | Englisch |
| Publikationsjahr: | November 2018 |
| Ort: | Cham |
| Verlag: | Springer International Publishing |
| Buchtitel: | Explainable and Interpretable Models in Computer Vision and Machine Learning |
| Veranstaltungsort: | Cham |
| DOI: | 10.1007/978-3-319-98131-4_4 |
| Kurzbeschreibung (Abstract): | Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 18 Dez 2019 08:31 |
| Letzte Änderung: | 28 Feb 2022 13:36 |
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