Name
Abstract | ||
|---|---|---|
We introduce Evolutionary Feature Synthesis (EFS), a regression method that generates readable, nonlinear models of small to medium size datasets in seconds. EFS is, to the best of our knowledge, the fastest regression tool based on evolutionary computation reported to date. The feature search involved in the proposed method is composed of two main steps: feature composition and feature subset selection. EFS adopts a bottom-up feature composition strategy that eliminates the need for a symbolic representation of the features and exploits the variable selection process involved in pathwise regularized linear regression to perform the feature subset selection step. The result is a regression method that is competitive against neural networks, and outperforms both linear methods and Multiple Regression Genetic Programming, up to now the best regression tool based on evolutionary computation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2739480.2754693 | Genetic and Evolutionary Computation Conference |
Keywords | Field | DocType |
Regression, Feature Synthesis, Feature Subset Selection | k-nearest neighbors algorithm,Feature vector,Feature selection,Pattern recognition,Computer science,Evolutionary computation,Genetic programming,Feature (machine learning),Artificial intelligence,Artificial neural network,Machine learning,Linear regression | Conference |
Citations | PageRank | References |
15 | 0.86 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Arnaldo | 1 | 81 | 7.69 |
| Una-May O'Reilly | 2 | 1477 | 181.38 |
| Kalyan Veeramachaneni | 3 | 716 | 61.50 |