# DTization: A New Method for Supervised Feature Scaling

arXiv: https://arxiv.org/abs/2404.17937 · HTML: https://mlsanity.com/paper/2404.17937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)

## Built on by (most cited first, 1 of 1)

- [Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction](https://mlsanity.com/paper/2508.05210/) (2025)

## Abstract (arXiv)

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.