Classification in Multi-Label Datasets
Multi-label datasets contain several classes, where each class can have multiple values. They appear in several domains such as music categorization into emotions and directed marketing. In this chapter, we are interested in the most popular task of Data Mining, which is the classification, more pre...
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| Autor principal: | |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/85471 |
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| Sumario: | Multi-label datasets contain several classes, where each class can have multiple values. They appear in several domains such as music categorization into emotions and directed marketing. In this chapter, we are interested in the most popular task of Data Mining, which is the classification, more precisely classification in multi-label datasets. To do this, we will present the different methods used to extract knowledge from these datasets. These methods are divided into two categories: problem transformation methods and algorithm adaptation ones. The methods of the first category transform multi-label classification problem into one or more single classification problems. While the methods of the second category extend a specific learning algorithm, in order to handle multi-label datasets directly. Also, we will present the different evaluation measures used to evaluate the quality of extracted knowledge. |
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| ISBN: | 978-1-80355-651-2 |
| DOI: | 10.5772/intechopen.109352 |