Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss
The LASSO-Clip-EN (LCEN) algorithm was previously introduced for nonlinear, interpretable feature selection and machine learning. However, its design and use was limited to regression tasks. In this work, we create a modified version of the LCEN algorithm that is suitable for classification tasks and maintains its desirable properties, such as interpretability. This modified LCEN algorithm is evaluated on four widely used binary and multiclass classification datasets. In these experiments, LCEN is compared against 10 other model types and consistently reaches high test-set macro F$_1$ score and Matthews correlation coefficient (MCC) metrics, higher than that of the majority of investigate...
| Authors | Pedro Seber, Richard D. Braatz |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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