Signal detection theory: valuable tools for evaluating inductive learning
Proceedings of the sixth international workshop on Machine learning
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Properties and benefits of calibrated classifiers
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Partial Ensemble Classifiers Selection for Better Ranking
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Active Learning to Maximize Area Under the ROC Curve
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Approximating the multiclass ROC by pairwise analysis
Pattern Recognition Letters
A critical analysis of variants of the AUC
Machine Learning
An Improved Model Selection Heuristic for AUC
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
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Learning Curves for the Analysis of Multiple Instance Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Repairing concavities in ROC curves
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A ROC-based reject rule for support vector machines
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Training multiclass classifiers by maximizing the volume under the ROC surface
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Efficient AUC learning curve calculation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The use of ROC Receiver Operating Characteristics analysis as a tool for evaluating the performance of classification models in machine learning has been increasing in the last decade. Among the most notable advances in this area are the extension of two-class ROC analysis to the multi-class case as well as the employment of ROC analysis in cost-sensitive learning. Methods now exist which take instance-varying costs into account. The purpose of our paper is to present a survey of this field with the aim of gathering important achievements in one place. In the paper, we present application areas of the ROC analysis in machine learning, describe its problems and challenges and provide a summarized list of alternative approaches to ROC analysis. In addition to presented theory, we also provide a couple of examples intended to illustrate the described approaches.