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Extracting Features from Textual Data in Class Imbalance Problems

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Extracting Features from Textual Data in Class Imbalance Problems

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Aravamuthan, S.; Jogalekar, P.; Lee, J. (2022). Extracting Features from Textual Data in Class Imbalance Problems. Journal of Computer-Assisted Linguistic Research. 6:42-58. https://doi.org/10.4995/jclr.2022.18200

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/191101

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Title: Extracting Features from Textual Data in Class Imbalance Problems
Author: Aravamuthan, Sarang Jogalekar, Prasad Lee, Jonghae
Issued date:
Abstract:
[EN] We address class imbalance problems. These are classification problems where the target variable is binary, and one class dominates over the other. A central objective in these problems is to identify features that ...[+]
Subjects: Class imbalance , Feature selection , N-gram frequency , NLP techniques , Random forest classifier
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Journal of Computer-Assisted Linguistic Research. (eissn: 2530-9455 )
DOI: 10.4995/jclr.2022.18200
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/jclr.2022.18200
Type: Artículo

References

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