SMOTE-CD: SMOTE for compositional data

PLoS One. 2023 Jun 29;18(6):e0287705. doi: 10.1371/journal.pone.0287705. eCollection 2023.

Abstract

Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After describing compositional data imbalance, this paper proposes an adaptation of the original Synthetic Minority Oversampling TEchnique (SMOTE) to deal with compositional data imbalance. The new approach, called SMOTE for Compositional Data (SMOTE-CD), generates synthetic examples by computing a linear combination of selected existing data points, using compositional data operations. The performance of the SMOTE-CD is tested with three different regressors (Gradient Boosting tree, Neural Networks, Dirichlet regressor) applied to two real datasets and to synthetic generated data, and the performance is evaluated using accuracy, cross-entropy, F1-score, R2 score and RMSE. The results show improvements across all metrics, but the impact of oversampling on performance varies depending on the model and the data. In some cases, oversampling may lead to a decrease in performance for the majority class. However, for the real data, the best performance across all models is achieved when oversampling is used. Notably, the F1-score is consistently increased with oversampling. Unlike the original technique, the performance is not improved when combining oversampling of the minority classes and undersampling of the majority class. The Python package smote-cd implements the method and is available online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acclimatization*
  • Benchmarking*
  • Entropy
  • Minority Groups
  • Neural Networks, Computer

Grants and funding

Funding was provided by the Energy Environment Solutions (E2S-UPPA: https://e2s-uppa.eu/fr/index.html) consortium and the international chair Kerrie Mengersen from E2S-UPPA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.