A New Variational Model for Binary Classification in the Supervised Learning Context
This work addresses binary classification for machine learning practitioners, but it appears incremental as it builds on existing variational methods without claiming major breakthroughs.
The authors tackled binary classification in supervised learning by developing a new variational model and deriving an optimality condition using functional analysis, then numerically solving it and comparing performance with other models using accuracy and AUC metrics.
We examine the supervised learning problem in its continuous setting and give a general optimality condition through techniques of functional analysis and the calculus of variations. This enables us to solve the optimality condition for the desired function u numerically and make several comparisons with other widely utilized supervised learning models. We employ the accuracy and area under the receiver operating characteristic curve as metrics of the performance. Finally, 3 analyses are conducted based on these two mentioned metrics where we compare the models and make conclusions to determine whether or not our method is competitive.