Exploring the Entire Regularization Path for the Asymmetric Cost Linear Support Vector Machine

TL;DR

This paper introduces an algorithm to explore the full regularization path of asymmetric-cost linear SVMs in two-dimensional hyperparameter space, enhancing understanding of model behavior across parameters.

Contribution

The paper presents a novel algorithm for the entire regularization path of asymmetric-cost linear SVMs in two-dimensional hyperparameter space, offering greater flexibility and insight.

Findings

Demonstrated the algorithm on synthetic data

Applied the method to real datasets

Showed improved understanding of model behavior

Abstract

We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms. The algorithms exploring the entire regularization paths have been proposed for single-cost support vector machines thereby providing the complete knowledge on the behavior of the trained model over the hyperparameter space. Considering the problem in two-dimensional hyperparameter space though enables our algorithm to maintain greater flexibility in dealing with special cases and sheds light on problems encountered by algorithms building the paths in one-dimensional spaces. We demonstrate two-dimensional regularization paths for linear support vector machines that we train on synthetic and real data.