2003 RankingLearningAlgorithms

Subject Headings: Ranking Leaning Algorithm algorithm recommendation - meta-learning - data characterization - ranking

Notes

We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, which was selected to represent properties that affect algorithm performance. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a ranking. The performance is assessed using a multicriteria evaluation measure that takes not only accuracy, but also time into account. As it is not common in Machine Learning to work with rankings, we had to identify and adapt existing statistical techniques to devise an appropriate evaluation methodology. Using that methodology, we show that the meta-learning method presented leads to significantly better rankings than the baseline ranking method. The evaluation methodology is general and can be adapted to other ranking problems. Although here we have concentrated on ranking classification algorithms, the meta-learning framework presented can provide assistance in the selection of combinations of methods or more complex problem solving strategies.

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 RankingLearningAlgorithms	Pavel B. Brazdil Carlos Soares Joaquim Pinto da Costa1			Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results.		Machine Learning Journal	http://www.liaad.up.pt/pub/2003/BSC03/BSC03.PDF	10.1023/A:1021713901879		2003