Determining the best classification algorithm with recourse to sampling and metalearning

• Type: Article in International Scientific Journal
• Date: 2010

Authors

Pavel Brazdil (Author) FEP; Leite, R (Author) Other

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: P-005-4DP

DOI: 10.1007/978-3-642-05177-7_8

Abstract (EN): Currently many classification algorithms exist and no algorithm exists that would outperform all the others. Therefore it is of interest to determine which classification algorithm is the best one for a given task. Although direct comparisons can be made for any given problem using a cross-validation evaluation, it is desirable to avoid this, as the computational costs are significant. We describe a method which relies on relatively fast pairwise comparisons involving two algorithms. This method is based on a previous work and exploits sampling landmarks, that is information about learning curves besides classical data characteristics. One key feature of this method is an iterative procedure for extending the series of experiments used to gather new information in the form of sampling landmarks. Metalearning plays also a vital role. The comparisons between various pairs of algorithm are repeated and the result is represented in the form of a partially ordered ranking. Evaluation is done by comparing the partial order of algorithm that has been predicted to the partial order representing the supposedly correct result. The results of our analysis show that the method has good performance and could be of help in practical applications. © 2010 Springer-Verlag Berlin Heidelberg.

Language: English

Type (Professor's evaluation): Scientific