A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification
This work addresses performance enhancement for related binary classification tasks, but it is incremental as it builds on existing multi-task learning methods with specific penalties.
The authors tackled multi-task binary classification by developing a fused logistic regression model with sparsity-inducing penalties to encode task similarity, achieving significant improvements over single-task learning in synthetic data regimes.
Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differences of related logistic regression models to encode similarity across related tasks. The resulting joint learning task is cast into a form that lends itself to be efficiently optimized with a recursive variant of the alternating direction method of multipliers. We show results on synthetic data and describe the regime of settings where our multi-task approach achieves significant improvements over the single task learning approach and discuss the implications on applying the fused logistic regression in different real world settings.