Using genetic programming to estimate performance of computational intelligence models

Abstract

This paper deals with the problem of choosing the most suitable model for a new data mining task. The metric is proposed on the data mining tasks space, and similar tasks are identified based on this metric.

A function estimating models performance on the new task from both the time and error point of view is evolved by means of genetic programming. The approach is verified on data containing results of several hundred thousands machine learning experiments.

Keywords

• data mining
• genetic programming
• metalearning
• performance prediction