The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Predicting the product purchase patterns of corporate customers
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
On the scalability of ordered multi-class ROC analysis
Computational Statistics & Data Analysis
A critical analysis of variants of the AUC
Machine Learning
Maximizing area under ROC curve for biometric scores fusion
Pattern Recognition
Hinge Rank Loss and the Area Under the ROC Curve
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient AUC Optimization for Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing new and better evaluation measures for machine learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ranking structured documents: a large margin based approach for patent prior art search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Training multiclass classifiers by maximizing the volume under the ROC surface
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Area under the ROC curve by bubble-sort approach (BSA)
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
An online AUC formulation for binary classification
Pattern Recognition
Evolving neural networks with maximum AUC for imbalanced data classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Local expert forest of score fusion for video event classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n2) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n.) This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.