Classification in Multi-Label Datasets

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Classification in Multi-Label Datasets
Συγγραφείς: Aouatef, Mahani
Πηγή: Information Systems Management ISBN: 9781803556512
Information Systems Management
Στοιχεία εκδότη: IntechOpen, 2023.
Έτος έκδοσης: 2023
Θεματικοί όροι: FOS: Computer and information sciences, Artificial intelligence, Class (philosophy), Economics, Multi-label classification, Pattern recognition (psychology), Computer science, Detection and Prevention of Phishing Attacks, Management, Task (project management), Artificial Intelligence, Categorization, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Machine learning, Text categorization, Multi-label Learning, Active Learning in Machine Learning Research, Data mining, Information Systems
Περιγραφή: 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.
Τύπος εγγράφου: Part of book or chapter of book
Other literature type
Γλώσσα: English
DOI: 10.5772/intechopen.109352
DOI: 10.60692/gtbe5-4p517
DOI: 10.60692/jj7js-dps50
Σύνδεσμος πρόσβασης: https://mts.intechopen.com/articles/show/title/classification-in-multi-label-datasets
Rights: CC BY
Αριθμός Καταχώρησης: edsair.doi.dedup.....b6a13f12cd932105e20fbb1f36a38abb
Βάση Δεδομένων: OpenAIRE