DTization: A New Method for Supervised Feature Scaling

TL;DR

This paper introduces DTization, a supervised feature scaling method using decision trees and robust scaler, which improves machine learning performance by considering feature importance during scaling.

Contribution

The paper presents a novel supervised feature scaling technique called DTization that leverages decision trees to enhance data preprocessing for machine learning.

Findings

Significant performance improvements over traditional scaling methods.

Effective on both classification and regression datasets.

Utilizes feature importance for tailored scaling.

Abstract

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.