A bi-level evolutionary approach for the multi-label detection of smelly classes
Sofien Boutaib, Maha Elarbi, Slim Bechikh, Fabio Palomba, Lamjed Ben Saïd · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
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.