Bigfoot Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors

DOI

10.2991/eusflat-19.2019.83How 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, the most informative patterns according to that model, with high areas times squared densities, are grown by hill-climbing from fragments of them, that a complete algorithm provides. At every iteration, an optimization problem (or its linear relaxation) is solved thanks to integer linear programming (or greedily). A forward selection then chooses among the discovered patterns a non-redundant subset that fits, but does not overfit, the tensor. Experiments show the proposal discovers high-quality patterns and outperforms state-of-the-art approaches when applied to 0/1 tensors, a special case.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Lucas José Carneiro Maciel
AU  - Jônatas Alves
AU  - Vinicius F. dos Santos
AU  - Loïc Cerf
PY  - 2019/08
DA  - 2019/08
TI  - Bigfoot Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 602
EP  - 609
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.83
DO  - 10.2991/eusflat-19.2019.83
ID  - CarneiroMaciel2019/08
ER  -