ncreasing 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 vari-
ables 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 con-
ditional 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 U nder the
Curve (AU C) 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