부산대학교 도서관

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the “curse of dimension”. To this end, in this paper, an evolutionary multitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task $T_{a}$ is constructed to assist the original high-dimensional task $T_{o}$. Specifically, in EMT-ROCCH, a low-dimensional assisted task $T_{a}$ is firstly created. Then, two populations, $P_{a}$ and $P_{o}$, are used to evolve tasks $T_{a}$ and $T_{o}$, respectively. During the evolution, a knowledge transfer from $P_{a}$ to $P_{o}$ is designed to transfer the useful knowledge from $P_{a}$ to accelerate the convergence of $P_{o}$. Moreover, a knowledge transfer from $P_{o}$ to $P_{a}$ is developed to utilize the useful knowledge in $P_{o}$ to repair the individuals in $P_{a}$, aiming to avoid $P_{a}$ being trapped into the local optima. Experiment results on 12 high-dimensional datasets have shown that compared with the state-of-the-arts, the proposed EMT-ROCCH could achieve ROCCH with higher quality.