AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learning problem. Batch learning methods for solving the kernelized version of this problem suffer from scalability and may not result in sparse classifiers. Recent years have witnessed an increased interest in the development of online or single-pass online learning algorithms that design a classifier by maximizing the AUC performance. The AUC performance of nonlinear classifiers, designed using online methods, is not comparable with that of nonlinear classifiers designed using batch learning algorithms on many real-world datasets. Motivated by these observations, we design a scalable algorithm for maximizing AUC performance by greedily adding the required number of basis functions into the classifier model. The resulting sparse classifiers perform faster inference. Our experimental results show that the level of sparsity achievable can be order of magnitude smaller than the Kernel RankSVM model without affecting the AUC performance much.
Learning to rank is an important supervised learning problem and has application in a variety of domains such as information retrieval and online advertising. Treating the all instances query number same, the set of preference pairs will be same as T and the Kernel rankSVM problem, discussed in @cite_8 is same as ). Kuo et. al. @cite_8 used trust region Newton method to solve this problem. This method stores the full kernel matrix as repeated kernel evaluations are bottleneck in Kernel rankSVM. This method has two drawbacks: 1) it is not scalable as the memory requirement is prohibitively high for large datasets, and 2) the learned model is not sparse resulting in computationally expensive predictions.
@cite_8
2400628046
Learning to rank is an important task for recommendation systems, online advertisement and web search. Among those learning to rank methods, rankSVM is a widely used model. Both linear and nonlinear (kernel) rankSVM have been extensively studied, but the lengthy training time of kernel rankSVM remains a challenging issue. In this paper, after discussing difficulties of training kernel rankSVM, we propose an efficient method to handle these problems. The idea is to reduce the number of variables from quadratic to linear with respect to the number of training instances, and efficiently evaluate the pairwise losses. Our setting is applicable to a variety of loss functions. Further, general optimization methods can be easily applied to solve the reformulated problem. Implementation issues are also carefully considered. Experiments show that our method is faster than state-of-the-art methods for training kernel rankSVM.