Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification

In this paper we present a Twin Parametric-Margin Support Vector Machine (TPMSVM) model to tackle the problem of multiclass classification. In the spirit of one-versus-all paradigm, for each class we construct a classifier by solving a TPMSVM-type model. Once all classifiers have been determined, they are combined into an aggregate decision function. We consider the cases of both linear and nonlinear kernel-induced classifiers. In addition, we robustify the proposed approach through robust optimization techniques. Indeed, in real-world applications observations are subject to measurement errors and noise, affecting the quality of the solutions. Consequently, data uncertainties need to be included within the model in order to prevent low accuracies in the classification process. Preliminary computational experiments on real-world datasets show the good performance of the proposed approach.

Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB

Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets. BO is particularly popular for solving numerical optimization problems in industry, where the evaluation of objective functions often relies on time-consuming simulations or physical experiments. However, many industrial problems depend on a large number of parameters. This poses a challenge for BO algorithms, whose performance is often reported to suffer when the dimension grows beyond 15 variables. Although many new algorithms have been proposed to address this problem, it is not well understood which one is the best for which optimization scenario. In this work, we compare five state-of-the-art high-dimensional BO algorithms, with vanilla BO and CMA-ES on the 24 BBOB functions of the COCO environment at increasing dimensionality, ranging from 10 to 60 variables. Our results confirm the superiority of BO over CMA-ES for limited evaluation budgets and suggest that the most promising approach to improve BO is the use of trust regions. However, we also observe significant performance differences for different function landscapes and budget exploitation phases, indicating improvement potential, e.g., through hybridization of algorithmic components.

Electre Tri-Machine Learning Approach to the Record Linkage Problem

In this short paper, the Electre Tri-Machine Learning Method, generally used to solve ordinal classification problems, is proposed for solving the Record Linkage problem. Preliminary experimental results show that, using the Electre Tri method, high accuracy can be achieved and more than 99% of the matches and nonmatches were correctly identified by the procedure.