publication . Preprint . Part of book or chapter of book . 2017

Scalable Nonlinear AUC Maximization Methods

Indrakshi Ray; Hamidreza Chitsaz; Majdi Khalid;
Open Access English
  • Published: 02 Oct 2017
Abstract
The area under the ROC curve (AUC) is a widely used measure for evaluating classification performance on heavily imbalanced data. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines because of their capability in modeling the complex nonlinear structures underlying most real-world data. However, the high training complexity renders the kernelized AUC machines infeasible for large-scale data. In this paper, we present two nonlinear AUC maximization algorithms that optimize linear classifiers over a finite-dimensional feature space constructed via the k-means Nystrom approximation. Our first a...
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doi: 10.1007/978-3-030-10928-8_18
Subjects
arXiv: Computer Science::Machine LearningComputer Science::Information RetrievalStatistics::Machine Learning
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Computer Science - Machine Learning, Scalability, Hinge loss, Algorithm, Maximization, Convergence (routing), Classifier (linguistics), Regularization (mathematics), Computer science, Feature vector, Pairwise comparison
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