Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/28844
Author(s): Farkhari, H.
Viana, J.
Campos, L. M.
Sebastião, P.
Bernardo, L.
Editor: Fonseca, N. L. S. da., Marca, J. R. B. da., Bregni, S., and Granville, L. Z.
Date: 2022
Title: New PCA-based category encoder for efficient data processing in IoT devices
Book title/volume: 2022 IEEE Globecom Workshops (GC Wkshps)
Pages: 789 - 795
Event title: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Reference: Farkhari, H., Viana, J., Campos, L. M., Sebastião, P., & Bernardo, L. (2022). New PCA-based category encoder for efficient data processing in IoT devices. In N. L. S. da Fonseca, J. R. B. da Marca, S. Bregni, & L. Z. Granville (Eds.), 2022 IEEE Globecom Workshops (GC Wkshps) (pp. 789-795). IEEE. https://doi.org/10.1109/GCWkshps56602.2022.10008757
ISBN: 978-1-6654-5975-4
DOI (Digital Object Identifier): 10.1109/GCWkshps56602.2022.10008757
Keywords: Categorical encoders
Dimensionality reduction
Internet of things
Feature selection
Machine learning
NSLKDD
Principal component analyses
Abstract: Increasing the cardinality of categorical variables might decrease the overall performance of machine learning (ML) algorithms. This paper presents a novel computational preprocessing method to convert categorical to numerical variables ML algorithms. It uses a supervised binary classifier to extract additional context-related features from the categorical values. The method requires two hyperparameters: a threshold related to the distribution of categories in the variables and the PCA representativeness. This paper applies the proposed approach to the well-known cybersecurity NSLKDD dataset to select and convert three categorical features to numerical features. After choosing the threshold parameter, we use conditional probabilities to convert the three categorical variables into six new numerical variables. Next, we feed these numerical variables to the PCA algorithm and select the whole or partial numbers of the Principal Components (PCs). Finally, by applying binary classification with ten different classifiers, we measure the performance of the new encoder and compare it with the other 17 well-known category encoders. The new technique achieves the highest performance related to accuracy and Area Under the Curve (AUC) on high cardinality categorical variables. Also, we define the harmonic average metrics to find the best trade-off between train and test performances and prevent underfitting and overfitting. Ultimately, the number of newly created numerical variables is minimal. This data reduction improves computational processing time in Internet of things (IoT) devices connected to future networks.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:IT-CRI - Comunicações a conferências internacionais

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