Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms
Machine Learning
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Measure—based classifier performance evaluation
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Neural Networks
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
Ensemble classification for constraint solver configuration
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Software quality trade-offs: A systematic map
Information and Software Technology
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Distributed self-organizing bandwidth allocation for priority-based bus communication
Concurrency and Computation: Practice & Experience
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
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The impact of learning algorithm optimization by means of parameter tuning is studied. To do this, two quality attributes, sensitivity and classification performance, are investigated, and two metrics for quantifying each of these attributes are suggested. Using these metrics, a systematic comparison has been performed between four induction algorithms on eight data sets. The results indicate that parameter tuning is often more important than the choice of algorithm and there does not seem to be a trade-off between the two quality attributes. Moreover, the study provides quantitative support to the assertion that some algorithms are more robust than others with respect to parameter configuration. Finally, it is briefly described how the quality attributes and their metrics could be used for algorithm selection in a systematic way.