Exploring multiobjective training in multiclass classification
Abstract
Multinomial logistic loss and L 2 regularization are often conflicting objectives as more robust regularization leads to restrained multinomial parameters. For many practical problems, leveraging the best of both worlds would be invaluable for better decision-making processes. This research proposes a novel framework to obtain representative and diverse L 2-regularized multinomial models, based on valuable trade-offs between prediction error and model complexity. The framework relies upon the Non-Inferior Set Estimation (NISE) method–a deterministic multiobjective solver. NISE automatically implements hyperparameter tuning in a multiobjective context. Given the diverse set of efficient learning models, model selection and aggregation of the multiple models in an ensemble framework promote high performance in multiclass classification. Additionally, NISE uses the weighted sum method as scalarization, thus …
