Using Artificial Intelligence for Model Selection

TL;DR

This paper evaluates the effectiveness of Adaptive Simulated Annealing (ASA) versus traditional regression methods for model selection in large, complex datasets, highlighting ASA's advantages in stability and performance.

Contribution

It introduces the application of ASA for model selection in large datasets and compares its performance with traditional regression methods.

Findings

ASA performs better on large, complex datasets.

Traditional regression methods are more effective on small datasets.

ASA demonstrates numerical stability in complex scenarios.

Abstract

We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the problem of analyzing data on a large population and selecting the best model to predict that an individual with various traits will have a particular disease. We compare ASA with traditional forward and backward regression on computer simulated data. We find that the traditional methods of modeling are better for smaller data sets whereas a numerically stable ASA seems to perform better on larger and more complicated data sets.