Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Exploiting AUC for optimal linear combinations of dichotomizers
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
An online AUC formulation for binary classification
Pattern Recognition
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition
Hi-index | 0.01 |
Classifier combination is a useful and common methodology to design an effective classification system. A large number of combination rules has been proposed hitherto, mostly aimed at minimizing the error rate. Recently, some methods have been presented that are devoted to maximize the area under the ROC curve (AUC), a more suitable performance measure when dealing with two-class problems with imprecise environment and/or imbalanced class priors. However, there are several applications that do not operate in the complete range of the ROC curve, but only in particular regions of it. In these cases, it is better to analyze the performance only in a part of the curve and to use the partial AUC (pAUC). This paper presents a new method that aims at maximizing the pAUC by means of linear combination of classifiers. The effectiveness of the proposed method has been proved on two biometric databases.