Online classifier adaptation for cost-sensitive learning

Abstract

In this paper, we propose the problem of online cost-sensitive classifier 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 problem 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 update 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.

Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection

“…Zhao et al (2011) proposed an extended cost-sensitive regression adjustment method to minimise the average error prediction under an asymmetric cost structure. Zhang and García (2015) developed a new classifier by adding an adaptation function to the base classifier and updating the adaptation function parameter according to the streaming data samples. As an advantage, it does not need to retrain the learning model and improve the agility of the prediction model.…”