Instance-Based Learning Algorithms
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Pretopological Approach for Supervised Learning
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An analysis of how training data complexity affects the nearest neighbor classifiers
Pattern Analysis & Applications
Top 10 algorithms in data mining
Knowledge and Information Systems
The lack of a priori distinctions between learning algorithms
Neural Computation
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
In search of targeted-complexity problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The landscape contest at ICPR 2010
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
An n-spheres based synthetic data generator for supervised classification
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Towards UCI+: A mindful repository design
Information Sciences: an International Journal
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The excellence of a given learner is usually claimed through a performance comparison with other learners over a collection of data sets. Too often, researchers are not aware of the impact of their data selection on the results. Their test beds are small, and the selection of the data sets is not supported by any previous data analysis. Conclusions drawn on such test beds cannot be generalised, because particular data characteristics may favour certain learners unnoticeably. This work raises these issues and proposes the characterisation of data sets using complexity measures, which can be helpful for both guiding experimental design and explaining the behaviour of learners.