Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors

Authors

Lucas Maciel, Jônatas Alves, Vinicius Fernandes dos Santos, Loïc Cerf^*,

Computer Science Department, Federal University of Minas Gerais, Avenida Antônio Carlos 6627 - Prédio do ICEx, Belo Horizonte, Minas Gerais 31270-901, Brazil

^*Corresponding author. Email: lcerf@dcc.ufmg.br

DOI

10.2991/ijcis.d.200715.002How to use a DOI?

Keywords

Disjunctive box cluster model; Fuzzy tensor; Hill-climbing; Integer Linear Programming; Forward selection

Abstract

Fuzzy tensors encode to what extent n-ary predicates are satisfied. The disjunctive box cluster model is a regression model where sub-tensors are explanatory variables for the values in the fuzzy tensor. In this article, locally optimal patterns for that model, with high areas times squared densities, are grown by hill-climbing from fragments of them. A forward selection then chooses among the discovered patterns a non-redundant subset that fits, but does not overfit, the tensor.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Lucas Maciel
AU  - Jônatas Alves
AU  - Vinicius Fernandes dos Santos
AU  - Loïc Cerf
PY  - 2020
DA  - 2020/07/29
TI  - Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors
JO  - International Journal of Computational Intelligence Systems
SP  - 1036
EP  - 1047
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200715.002
DO  - 10.2991/ijcis.d.200715.002
ID  - Maciel2020
ER  -