Least Squares Support Vector Machine Classifiers
Neural Processing Letters
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
An efficient algorithm for learning to rank from preference graphs
Machine Learning
| Hi-index | 0.00 |
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 RLS-type binary classifier that maximizes an approximation of AUC and has a closed-form 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.