Friday, 11:30 - 11:55 h, Room: H 2013

 

Amaya Nogales Gómez
Matheuristics for Ψ-learning

Coauthors: Emilio Carrizosa, Dolores Romero Morales

 

Abstract:
The ψ-learning classifier is an alternative to the Support Vector Machine classifier, which uses the so-called ramp loss function. The ψ-learning classifier is expected to be more robust, and therefore to have a better performance, when outliers are present.
A Nonlinear Mixed Integer Programming formulation proposed in the literature is analysed. Solving the problem exactly is only possible for data sets of very small size. For datasets of more realistic size, the state-of-the-art is a recent matheuristic, which attempts to solve the MINLP imposing a time limit.
In this talk, a new matheuristic, based on the solution of much simpler Convex Quadratic Problems and Linear Integer Problems, is developed. Computational results are given showing the improvement against the state-of-the-art method.

 

Talk 3 of the invited session Fri.1.H 2013
"Integer programming in data mining" [...]
Cluster 11
"Integer & mixed-integer programming" [...]

 

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