C4.5: programs for machine learning
C4.5: programs for machine learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Modern Applied Statistics with S
Modern Applied Statistics with S
Benchmark testing of algorithms for very robust regression: FS, LMS and LTS
Computational Statistics & Data Analysis
Introduction to face recognition and evaluation of algorithm performance
Computational Statistics & Data Analysis
Editorial: Special issue on statistical algorithms and software in R
Computational Statistics & Data Analysis
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It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data-such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new formal framework for describing the relationship between data set characteristics and the performance of different learning algorithms is proposed. The framework combines the advantages of benchmark experiments with the formal description of data set characteristics by means of statistical and information-theoretic measures and with the recursive partitioning of Bradley-Terry models for comparing the algorithms' performances. The formal aspects of each component are introduced and illustrated by means of an artificial example. Its real-world usage is demonstrated with an application example consisting of thirteen widely-used data sets and six common learning algorithms. The Appendix provides information on the implementation and the usage of the framework within the R language.