Title :
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
Author :
Lee, Jun Won ; Carrier, Christophe Giraud
Author_Institution :
Dept. of Comput. Sci., Brigham Young Univ., Provo, UT
Abstract :
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-features whose values are directly relevant to certain ranges of predictive accuracy for 7 learning algorithms on 135 UCI datasets. Discretization of these 4 meta-features based on thresholds derived from our analysis significantly boosts the accuracy of the meta-level classification task.
Keywords :
computational complexity; learning (artificial intelligence); pattern classification; UCI datasets; algorithm accuracy; computational complexity; learning algorithms; meta-features; meta-learning; meta-level classification task; model selection; visual analysis; Accuracy; Algorithm design and analysis; Application software; Clustering algorithms; Computational complexity; Data analysis; Machine learning; Machine learning algorithms; Prediction algorithms; Spatial databases; Accuracy Prediction; Meta-features; Meta-learning; Visual Analysis;
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
DOI :
10.1109/ICMLA.2008.62
