Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
Multiobjective optimization of safety related systems: an application to short-term conflict alert
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximating the multiclass ROC by pairwise analysis
Pattern Recognition Letters
ROC analysis in ordinal regression learning
Pattern Recognition Letters
Analysis of Two-Dimensional Non-Rigid Shapes
International Journal of Computer Vision
On the scalability of ordered multi-class ROC analysis
Computational Statistics & Data Analysis
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
International Journal of Computer Vision
Information Processing Letters
A novel measure for evaluating classifiers
Expert Systems with Applications: An International Journal
Paretian similarity for partial comparison of non-rigid objects
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
The ROC skeleton for multiclass ROC estimation
Pattern Recognition Letters
An adaptive multiobjective approach to evolving ART architectures
IEEE Transactions on Neural Networks
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Using evolutionary multiobjective techniques for imbalanced classification data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
Visualizing 4D approximation sets of multiobjective optimizers with prosections
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Comparing multi-objective and threshold-moving ROC curve generation for a prototype-based classifier
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid
International Journal of Data Mining and Bioinformatics
ROC analysis of classifiers in machine learning: A survey
Intelligent Data Analysis
Hi-index | 0.00 |
The receiver operating characteristic (ROC) has become a standard tool for the analysis and comparison of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we discuss and present an extension to the standard two-class ROC for multi-class problems. We define the ROC surface for the Q-class problem in terms of a multi-objective optimisation problem in which the goal is to simultaneously minimise the Q(Q-1) misclassification rates, when the misclassification costs and parameters governing the classifier's behaviour are unknown. We present an evolutionary algorithm to locate the Pareto front-the optimal trade-off surface between misclassifications of different types. The use of the Pareto optimal surface to compare classifiers is discussed and we present a straightforward multi-class analogue of the Gini coefficient. The performance of the evolutionary algorithm is illustrated on a synthetic three class problem, for both k-nearest neighbour and multi-layer perceptron classifiers.