Abstract
This book is focused on multilabel classification and related topics. Multilabel classification is one specific type of classification, classification being one of the usual tasks in the data mining field. Data mining itself can be seen as a step into a broad process, the discovery of new knowledge from databases. The goal of this first chapter is to introduce all these concepts, aiming to set the working context for the topics covered in the following ones. A global outline to this respect is given in Sect. 1.1. Section 1.2 provides an overview of the whole Knowledge Discovery in Databases process. Section 1.3 introduces the essential preprocessing tasks. Then, the different learning styles in use nowadays are explained in Sect. 1.4, and lastly multilabel classification is introduced in comparison with other traditional types of classification in Sect. 1.5.
Keywords
- Multilabel Classification
- Pre-processing Tasks
- Multiple Instance Learning
- Continuous Numeric Values
- Extreme Value Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J. (2016). Introduction. In: Multilabel Classification . Springer, Cham. https://doi.org/10.1007/978-3-319-41111-8_1
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