POLYMOT versus HILOMOT - A Comparison of Two Different Training Algorithms for Local Model Networks

Abstract

A comparison of the POLYnomial MOdel Tree (POLYMOT) and the HIerarchical LOcal MOdel Tree (HILOMOT) algorithm for the construction of local model networks is presented in this paper. A comprehensive benchmark study with different 2-dimensional test functions as well as four popular measured datasets demonstrates the robustness against noise and overfitting of both algorithms. The major number of axes-oblique local linear models by using HILOMOT is often compensated by a smaller number of more complex axes-orthogonal local polynomial models by using POLYMOT, so that both methods generate the same model quality. However, for high-dimensional input spaces HILOMOT demonstrates its advantage of axis-oblique partitioning.