# SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features

arXiv: https://arxiv.org/abs/2103.07612 · HTML: https://mlsanity.com/paper/2103.07612/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models](https://mlsanity.com/paper/2404.12404/) (2024)
- [A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)](https://mlsanity.com/paper/2402.17398/) (2024)
- [Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly](https://mlsanity.com/paper/2405.18845/) (2024)
- [Interpretable classification of wiki-review streams](https://mlsanity.com/paper/2405.18335/) (2024)
- [Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring](https://mlsanity.com/paper/2503.22730/) (2025)
- [Bond Default Prediction with Text Embeddings, Undersampling and Deep Learning](https://mlsanity.com/paper/2110.07035/) (2021)
- [A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters](https://mlsanity.com/paper/2309.00993/) (2023)
- [Beyond the Request: Harnessing HTTP Response Headers for Cross-Browser Web Tracker Classification in an Imbalanced Setting](https://mlsanity.com/paper/2402.01240/) (2024)
- [A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction](https://mlsanity.com/paper/2501.11293/) (2025)
- [Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study](https://mlsanity.com/paper/2505.06264/) (2025)
- [CTTVAE: Latent Space Structuring for Conditional Tabular Data Generation on Imbalanced Datasets](https://mlsanity.com/paper/2602.03641/) (2026)

## Abstract (arXiv)

Real world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these under-represented instances. To solve this problem, many variations of synthetic minority over-sampling methods (SMOTE) have been proposed to balance the dataset which deals with continuous features. However, for datasets with both nominal and continuous features, SMOTE-NC is the only SMOTE-based over-sampling technique to balance the data. In this paper, we present a novel minority over-sampling method, SMOTE-ENC (SMOTE - Encoded Nominal and Continuous), in which, nominal features are encoded as numeric values and the difference between two such numeric value reflects the amount of change of association with minority class. Our experiments show that the classification model using SMOTE-ENC method offers better prediction than model using SMOTE-NC when the dataset has a substantial number of nominal features and also when there is some association between the categorical features and the target class. Additionally, our proposed method addressed one of the major limitations of SMOTE-NC algorithm. SMOTE-NC can be applied only on mixed datasets that have features consisting of both continuous and nominal features and cannot function if all the features of the dataset are nominal. Our novel method has been generalized to be applied on both mixed datasets and on nominal only datasets. The code is available from mkhushi.github.io