The ALAMO approach to machine learning

• Published in: Computers & chemical engineering 2017-11, Vol.106, p.785-795
• Main Authors: Wilson, Zachary T., Sahinidis, Nikolaos V.
• Format: Article
• Language: English
• Subjects: Feature selection; Mixed-integer optimization; Model selection; Parametric regression
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2017.02.010

•The ALAMO framework for building models is reviewed.•A review of the machine learning literature on model-building is presented.•ALAMO is illustrated through its application to learning problems in kinetics. ALAMO is a computational methodology for learning algebraic functions from data. Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables. Linear combinations of these non-linear transformations allow a linear model to better approximate complex behavior observed in real processes. The model is refined, as additional data are obtained in an adaptive fashion through error maximization sampling using derivative-free optimization. Models built using ALAMO can enforce constraints on the response variables to incorporate first-principles knowledge. The ability of ALAMO to generate simple and accurate models for a number of reaction problems is demonstrated. The error maximization sampling is compared with Latin hypercube designs to demonstrate its sampling efficiency. ALAMO's constrained regression methodology is used to further refine concentration models, resulting in models that perform better on validation data and satisfy upper and lower bounds placed on model outputs.