Adaptive classification under computational budget constraints using sequential data gathering

Abstract

Classification algorithms often handle large amounts of labeled data. When a label is the result of a very expensive computer experiment (in terms of computational time), sequential selection of samples can be used to limit the overall cost of acquiring the labeled data. This paper outlines the concept of sequential design for classification, and the extension of an existing stateof-the-art research platform for surrogate modeling to handle classification problems with sequential design. The capabilities of the platform are illustrated on a number of use cases including real-world applications such as an ElectroMagnetic Compatibility (EMC) and a Computational Fluid Dynamics (CFD) problem. The CFD problem also illustrates how classification can be used together with regression techniques to solve multi-objective constrained optimization problems of complex systems.

Tool for Recovering after Meteorological Events Using a Real-Time REM and IoT Management Platform

“…To optimize the deployment the SUMO was used [32,33]. The SUMO is a MATLAB tool that automatically builds accurate surrogate models (also known as meta-models or response surface models) of a given data source (simulation code, dataset, script, etc.)…”

“…within the accuracy and time constraints set by the user. The tool minimizes the number of data points (which it chooses automatically) and tries to be as adaptive and autonomous as possible, requiring no user input besides some initial configuration [32,33].…”

Optimized statistical test for event detection in non-intrusive load monitoring

“…SBO has already been applied successfully in other research areas, like e.g., wireless communication [13], electromagnetics [10], and microwave filter design [14]. An implementation of the SBO routine is available in the Surrogate Modelling (SUMO) Toolbox [15], [16] (available online 1 ).…”

Constrained multi‐objective antenna design optimization using surrogates

“…Surrogate-based optimization (SBO) is distinct from surrogate modeling. It is a popular choice to expedite complex optimization problems [11][12][13] involving expensive simulations. The SBO involves generating surrogate models of the underlying system (eg, a simulation model) on the fly.…”