Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Goal-Directed Classification Using Linear Machine Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on core research issues for statistical pattern recognition
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Estimating the Predictive Accuracy of a Classifier
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Data Complexity Analysis for Classifier Combination
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Multiresolution Estimates of Classification Complexity
IEEE Transactions on Pattern Analysis and Machine Intelligence
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Experimental study for the comparison of classifier combination methods
Pattern Recognition
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Intelligence and computation: a view from physiology
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
A fast computation of inter-class overlap measures using prototype reduction schemes
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
An empirical evaluation on dimensionality reduction schemes for dissimilarity-based classifications
Pattern Recognition Letters
Predicting problem difficulty for genetic programming applied to data classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
How many neurons?: a genetic programming answer
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Estimating classifier performance with genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Prediction of classifier training time including parameter optimization
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Classifier selection based on data complexity measures
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Execution engine of meta-learning system for KDD in multi-agent environment
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Efficient feature size reduction via predictive forward selection
Pattern Recognition
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
A feature subset selection algorithm automatic recommendation method
Journal of Artificial Intelligence Research
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Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.