An Application of Expectation-Maximization for Model Verification

Abstract

A description which summarizes entire and usually big set of data is called its model. The problem investigated in the paper consists in verification of models of data coming from a simulation experiment of selecting candidates for operators of mobile robot (more strictly building reliable predictive model of the data). The models are validated using train-and-test method and verified with the help of the EM (expectation-maximization) algorithm which was originally designed for solving clustering problems with missing data. Actually, the selecting is a clustering problem because the candidates are assigned to 'chosen', 'accepted' or 'rejected' subgroups. For such a case the missing data is the category (the subgroup) for which a candidate should be assigned on the basis of his activity measured during the simulation experiment. The paper explains the procedure of model verification. It also shows experimental results and draws conclusions.