Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Machine Learning
A Comparison of Ranking Methods for Classification Algorithm Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Ranking with Predictive Clustering Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
A Comparative Study of Some Issues Concerning Algorithm Recommendation Using Ranking Methods
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Feature Selection for Meta-learning
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
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
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Given the wide variety of available classification algorithms and the volume of data today's organizations need to analyze, the selection of the right algorithm to use on a new problem is an important issue. In this paper we present a combination of techniques to address this problem. The first one, zooming, analyzes a given dataset and selects relevant (similar) datasets that were processed by the candidate algoritms in the past. This process is based on the concept of "distance", calculated on the basis of several dataset characteristics. The information about the performance of the candidate algorithms on the selected datasets is then processed by a second technique, a ranking method. Such a method uses performance information to generate advice in the form of a ranking, indicating which algorithms should be applied in which order. Here we propose the adjusted ratio of ratios ranking method. This method takes into account not only accuracy but also the time performance of the candidate algorithms. The generalization power of this ranking method is analyzed. For this purpose, an appropriate methodology is defined. The experimental results indicate that on average better results are obtained with zooming than without it.