In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2023-6-29), p. e0287705-
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0287705
DOI:
10.1371/journal.pone.0287705.g001
DOI:
10.1371/journal.pone.0287705.g002
DOI:
10.1371/journal.pone.0287705.g003
DOI:
10.1371/journal.pone.0287705.g004
DOI:
10.1371/journal.pone.0287705.g005
DOI:
10.1371/journal.pone.0287705.g006
DOI:
10.1371/journal.pone.0287705.g007
DOI:
10.1371/journal.pone.0287705.t001
DOI:
10.1371/journal.pone.0287705.t002
DOI:
10.1371/journal.pone.0287705.t003
DOI:
10.1371/journal.pone.0287705.t004
DOI:
10.1371/journal.pone.0287705.s001
DOI:
10.1371/journal.pone.0287705.s002
DOI:
10.1371/journal.pone.0287705.s003
DOI:
10.1371/journal.pone.0287705.s004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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