2022

Conférence

Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO)

This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.

@inproceedings{boutaib2022bilevel,
author = {Boutaib, Sofien and Elarbi, Maha and Bechikh, Slim and Palomba, Fabio and Ben Said, Lamjed},
title = {A Bi-Level Evolutionary Approach for the Multi-Label Detection of Smelly Classes},
booktitle = {GECCO ’22: Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {782--785},
year = {2022},
publisher = {Association for Computing Machinery (ACM)},
isbn = {978-1-4503-9268-6},
doi = {10.1145/3520304.3528946}
}