  Artificial intelligence is currently a dominant force in shaping various
aspects of the world. Machine learning is a sub-field in artificial
intelligence. Feature scaling is one of the data pre-processing techniques that
improves the performance of machine learning algorithms. The traditional
feature scaling techniques are unsupervised where they do not have influence of
the dependent variable in the scaling process. In this paper, we have presented
a novel feature scaling technique named DTization that employs decision tree
and robust scaler for supervised feature scaling. The proposed method utilizes
decision tree to measure the feature importance and based on the importance,
different features get scaled differently with the robust scaler algorithm. The
proposed method has been extensively evaluated on ten classification and
regression datasets on various evaluation matrices and the results show a
noteworthy performance improvement compared to the traditional feature scaling
methods.
