Name
Abstract | ||
|---|---|---|
Currently, there is no solution, which does not require a high runtime, to the problem of choosing preprocessing methods, feature selection algorithms and classifiers for a supervised learning problem. In this paper we present a method for efficiently finding a combination of algorithms and parameters that effectively describes a dataset. Furthermore, we present an optimization technique, based on ParamILS, which can be used in other contexts where each evaluation of the objective function is highly time consuming, but an estimate of this function is possible. In this paper, we present our algorithm and initial validation of it over real and synthetic data. In said validation, our proposal demonstrates a significant reduction in runtime, compared to ParamILS, while solving problems with these characteristics. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25085-9_80 | CIARP |
Keywords | Field | DocType |
initial validation,optimization technique,significant reduction,full model selection,feature selection algorithm,supervised learning problem,preprocessing method,objective function,synthetic data,high runtime,probabilistic iterative local search,time consuming | Data mining,Feature selection,Pattern recognition,Computer science,Model selection,Supervised learning,Synthetic data,Preprocessor,Artificial intelligence,Probabilistic logic,Local search (optimization),Machine learning | Conference |
Volume | ISSN | Citations |
7042 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esteban Cortazar | 1 | 0 | 0.34 |
| Domingo Mery | 2 | 466 | 42.09 |