Authors:
Roberto Delamora
1
;
2
;
Bruno Coelho
1
and
Jodelson Sabino
3
Affiliations:
1
Graduate Program in Instrumentation, Control and Automation of Mining Process, Federal University of Ouro Preto, Instituto Tecnológico Vale, Ouro Preto, Brazil
;
2
Vale S.A., Nova Lima, Brazil
;
3
Artificial Intelligence Center, Vale S.A., Vitória, Espirito Santo, Brazil
Keyword(s):
Wrapper-Filter Method, Ant Colony Optimization, Metaheuristic, Machine-Learning, Feature Selection, Dimensionality Reduction.
Abstract:
Attribute selection is a process by which the best subset of attributes in a given dataset is searched. In a world where decisions are increasingly based on data, it is essential to develop tools that allow this selection of attributes to be more efficiently performed, aiming to improve the final performance of the models. Ant colony optimization (ACO) is a well-known metaheuristic algorithm with several applications and recent versions developed for feature selection (FS). In this work, we propose an improvement in the general construction of ACO, with improvements and adjustments for subset evaluation in the original Rank-based version by BulInheimer et al. to increase overall efficiency. The proposed approach was evaluated on several real-life datasets taken from the UCI machine-learning repository, using various classifier models. The experimental results were compared with the recently published WFACOFS method by Ghosh et al., which shows that our method outperforms WFACOFS in m
ost cases.
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