Online classifier adaptation for cost-sensitive learning

TL;DR

This paper introduces the first online algorithm for adapting cost-sensitive classifiers to new cost settings using streaming data, improving performance and efficiency.

Contribution

It proposes a novel online adaptation method that updates a classifier based on streaming data and misclassification costs, addressing a gap in cost-sensitive learning.

Findings

Outperforms existing online and offline algorithms in classification accuracy

Requires significantly less running time than previous methods

Effective in adapting to new cost settings with streaming data

Abstract

In this paper, we propose the problem of online cost-sensitive clas- sifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The prob- lem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we up- date the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time.