TY - GEN

T1 - Efficient AUC maximization with regularized least-squares

AU - Pahikkala, Tapio

AU - Airola, Antti

AU - Suominen, Hanna

AU - Boberg, Jorma

AU - Salakoski, Tapio

PY - 2008

Y1 - 2008

N2 - Area under the receiver operating characteristics curve (AUC) is a popular measure for evaluating the quality of binary classifiers, and intuitively, machine learning algorithms that maximize an approximation of AUC should have a good AUC performance when classifying new examples. However, designing such algorithms in the framework of kernel methods has proven to be challenging. In this paper, we address AUC maximization with the regularized least-squares (RLS) algorithm also known as the least-squares support vector machine. First, we introduce RLStype binary classifier that maximizes an approximation of AUC and has a closedform solution. Second, we show that this AUC-RLS algorithm is computationally as efficient as the standard RLS algorithm that maximizes an approximation of the accuracy. Third, we compare the performance of these two algorithms in the task of assigning topic labels for newswire articles in terms of AUC. Our algorithm outperforms the standard RLS in every classification experiment conducted. The performance gains are most substantial when the distribution of the class labels is unbalanced. In conclusion, modifying the RLS algorithm to maximize the approximation of AUC does not increase the computational complexity, and this alteration enhances the quality of the classifier.

AB - Area under the receiver operating characteristics curve (AUC) is a popular measure for evaluating the quality of binary classifiers, and intuitively, machine learning algorithms that maximize an approximation of AUC should have a good AUC performance when classifying new examples. However, designing such algorithms in the framework of kernel methods has proven to be challenging. In this paper, we address AUC maximization with the regularized least-squares (RLS) algorithm also known as the least-squares support vector machine. First, we introduce RLStype binary classifier that maximizes an approximation of AUC and has a closedform solution. Second, we show that this AUC-RLS algorithm is computationally as efficient as the standard RLS algorithm that maximizes an approximation of the accuracy. Third, we compare the performance of these two algorithms in the task of assigning topic labels for newswire articles in terms of AUC. Our algorithm outperforms the standard RLS in every classification experiment conducted. The performance gains are most substantial when the distribution of the class labels is unbalanced. In conclusion, modifying the RLS algorithm to maximize the approximation of AUC does not increase the computational complexity, and this alteration enhances the quality of the classifier.

UR - http://www.scopus.com/inward/record.url?scp=84867510815&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84867510815

SN - 9781586038670

T3 - Frontiers in Artificial Intelligence and Applications

SP - 12

EP - 19

BT - 10th Scandinavian Conference on Artificial Intelligence, SCAI 2008

PB - IOS Press BV

ER -