Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Robust Classification for Imprecise Environments
Machine Learning
Discriminative Dimensionality Reduction Based on Generalized LVQ
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
An introduction to ROC analysis
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
Approximating the multiclass ROC by pairwise analysis
Pattern Recognition Letters
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Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all possible operating conditions, i.e., for all preferences regarding the tradeoff between false positives and false negatives. The generation of a ROC curve generally involves the training of a single classifier for a given set of operating conditions, with the subsequent use of threshold-moving to obtain a complete ROC curve. Recent work has shown that the generation of ROC curves may also be formulated as a multi-objective optimization problem in ROC space: the goals to be minimized are the false positive and false negative rates. This technique also produces a single ROC curve, but the curve may derive from operating points for a number of different classifiers. This paper aims to provide an empirical comparison of the performance of both of the above approaches, for the specific case of prototype-based classifiers. Results on synthetic and real domains shows a performance advantage for the multi-objective approach.