arXiv subjects

Antonio M. Rodríguez-Chía

Publications and source records attributed to Antonio M. Rodríguez-Chía.

21 records · Page 2Linked to original sources

arXiv2018

Mixed Integer Linear Programming for Feature Selection in Support Vector Machine

This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.

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arXiv2018

The stratified p-center problem

This work presents an extension of the p-center problem. In this new model, called Stratified p-Center Problem (SpCP), the demand is concentrated in a set of sites and the population of these sites is divided into different strata depending on the kind of service that they require. The aim is to locate p centers to cover the different types of services demanded minimizing the weighted average of the largest distances associated with each of the different strata. In addition, it is considered that more than one stratum can be present at each site. Different formulations, valid inequalities and preprocessings are developed and compared for this problem. An application of this model is presented in order to implement a heuristic approach based on the Sample Average Approximation method (SAA) for solving the probabilistic p-center problem in an efficient way.

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arXiv2017

On $\ell_p$-Support Vector Machines and Multidimensional Kernels

In this paper, we extend the methodology developed for Support Vector Machines (SVM) using $\ell_2$-norm ($\ell_2$-SVM) to the more general case of $\ell_p$-norms with $p\ge 1$ ($\ell_p$-SVM). The resulting primal and dual problems are formulated as mathematical programming problems; namely, in the primal case, as a second order cone optimization problem and in the dual case, as a polynomial optimization problem involving homogeneous polynomials. Scalability of the primal problem is obtained via general transformations based on the expansion of functionals in Schauder spaces. The concept of Kernel function, widely applied in $\ell_2$-SVM, is extended to the more general case by defining a new operator called multidimensional Kernel. This object gives rise to reformulations of dual problems, in a transformed space of the original data, which are solved by a moment-sdp based approach. The results of some computational experiments on real-world datasets are presented showing rather good behavior in terms of standard indicators such a \textit{accuracy index} and its ability to classify new data.

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