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Design of poset forest-based algorithms for the U-curve optimization problem

Grant number: 16/25959-7
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2017
Effective date (End): December 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Marcelo da Silva Reis
Grantee:Gustavo Estrela de Matos
Home Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
Associated research grant:13/07467-1 - CeTICS - Center of Toxins, Immune-Response and Cell Signaling, AP.CEPID

Abstract

The U-curve problem is a formulation of an optimization problem that can be used in the feature selection step of Machine Learning, with applications in the designing of computational models of biological systems. Nevertheless, the solutions so far proposed to tackle this problem have limitations from both required computational time and space points of view, which implies in the need for development of new algorithms. To this end, it was introduced in 2012 the Poset-Forest-Search (PFS) algorithm, which organizes the search space in forests of posets. This algorithm was implemented and tested, with promising results; however, new improvements are required until PFS becomes a competitive alternative to tackle the U-curve problem. In this project, we propose the design of a parallelized, scalable version of the PFS algorithm, using reduced ordered binary decision diagrams. Moreover, we propose to adapt PFS as an approximation algorithm, in which the approximation criterion to the optimal solution makes use of the Ockham's razor theorem. The developed algorithms will be implemented and tested on artificial instances and also on collection of datasets that are suitable for benchmarking of feature selection algorithms. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ESTRELA, GUSTAVO; GUBITOSO, MARCO DIMAS; FERREIRA, CARLOS EDUARDO; BARRERA, JUNIOR; REIS, MARCELO S. An Efficient, Parallelized Algorithm for Optimal Conditional Entropy-Based Feature Selection. Entropy, v. 22, n. 4 APR 2020. Web of Science Citations: 0.
REIS, MARCELO S.; ESTRELA, GUSTAVO; FERREIRA, CARLOS EDUARDO; BARRERA, JUNIOR. featsel: A framework for benchmarking of feature selection algorithms and cost functions. SOFTWAREX, v. 6, p. 193-197, 2017. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.