Title 



On the importance of data balancing for symbolic regression
 
Author 



 
Abstract 



Symbolic regression of inputoutput data conventionally treats data records equally. We suggest a framework for automatic assignment of weights to data samples, which takes into account the sample's relative importance. In this paper, we study the possibilities of improving symbolic regression on reallife data by incorporating weights into the fitness function. We introduce four weighting schemes defining the importance of a point relative to proximity, surrounding, remoteness, and nonlinear deviation from k nearestintheinputspace neighbors. For enhanced analysis and modeling of large imbalanced data sets we introduce a simple multidimensional iterative technique for subsampling. This technique allows a sensible partitioning (and compression) of data to nested subsets of an arbitrary size in such a way that the subsets are balanced with respect to either of the presented weighting schemes. For cases where a given inputoutput data set contains some redundancy, we suggest an approach to considerably improve the effectiveness of regression by applying more modeling effort to a smaller subset of the data set that has a similar information content. Such improvement is achieved due to better exploration of the search space of potential solutions at the same number of function evaluations. We compare different approaches to regression on five benchmark problems with a fixed budget allocation. We demonstrate that the significant improvement in the quality of the regression models can be obtained either with the weighted regression, exploratory regression using a compressed subset with a similar information content, or exploratory weighted regression on the compressed subset, which is weighted with one of the proposed weighting schemes.   
Language 



English
 
Source (journal) 



IEEE transactions on evolutionary computation / IEEE Neural Networks Council.  New York, N.Y.  
Publication 



New York, N.Y. : 2010
 
ISSN 



1089778X
 
Volume/pages 



14:2(2010), p. 252277
 
ISI 



000276069700006
 
Full text (Publishers DOI) 


  
