New PCA-based Category Encoder for Efficient Data Processing in IoT Devices

Hamed Farkhari, Joseanne Viana, Luís Miguel Campos, Pedro Sebastião, Luís Bernardo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
Place of PublicationNew Jersey
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-6654-5975-4
ISBN (Print)978-1-6654-5976-1
Publication statusPublished - 2022
Event2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

Publication series

Name2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings


Conference2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
CityVirtual, Online


  • Categorical Encoders
  • Dimensionality Reduction
  • Feature Selection
  • Internet of things
  • Machine Learning
  • Principal Component Analyses


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