MOVIE GENRE PREDICTION USING MACHINE LEARNING
DOI:
https://doi.org/10.24867/30BE13SantracKeywords:
film;, genre prediction, multilabel classificationAbstract
When creating new films, information often leaks. Such information is incomplete and can reveal details like who is working on the film, when it will premiere, the film's title, etc. To get a complete picture of the film, it's necessary to know the genre it belongs to. In this study, genre prediction is performed using Multilabel classifiers (Multilabel k-Nearest Neighbors, Classifier Chains, Binary Relevance) based on information about the people working on the film, their roles, the film's title, the year of its premiere, and whether the film is intended for children. The prediction results are evaluated using Micro-average and Macro-average evaluation methods.
References
[1] Piotr Szymański, Tomasz Kajdanowicz (2017) Multi-label embedding-based classification Available: http://scikit.ml/multilabelembeddings.html[datum pristupa 10.07.2024.]
[2] D.W. Aha, Special AI review issue on lazy learning, Artif. Intell. Review 11
[3] Zhang, Min-Ling, et al. "Binary relevance for multi-label learning: an overview." Frontiers of Computer Science 12.2 (2018): 191-202.
[4] Read, Jesse, et al. "Classifier chains for multi-label classification." Machine learning 85.3 (2011): 333-359.
[5] [Online]Available: https://www.imdb.com/interfaces/
[datum pristupa 10.07.2024.]
[2] D.W. Aha, Special AI review issue on lazy learning, Artif. Intell. Review 11
[3] Zhang, Min-Ling, et al. "Binary relevance for multi-label learning: an overview." Frontiers of Computer Science 12.2 (2018): 191-202.
[4] Read, Jesse, et al. "Classifier chains for multi-label classification." Machine learning 85.3 (2011): 333-359.
[5] [Online]Available: https://www.imdb.com/interfaces/
[datum pristupa 10.07.2024.]
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Published
2025-04-04
Issue
Section
Electrotechnical and Computer Engineering