1 code implementation • 7 Sep 2023 • Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota
In the clustering domain, various algorithms with a federated learning framework (i. e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy.
1 code implementation • 1 May 2023 • Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi, Stefan Wermter
In general, a similarity threshold (i. e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance.
no code implementations • 22 Apr 2022 • Takato Kinoshita, Naoki Masuyama, Yiping Liu, Yusuke Nojima, Hisao Ishibuchi
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs).
1 code implementation • 18 Mar 2022 • Naoki Masuyama, Yusuke Nojima, Farhan Dawood, Zongying Liu
This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm.
no code implementations • 26 Jan 2022 • Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi
In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm.
no code implementations • 15 Oct 2021 • Victor Villin, Naoki Masuyama, Yusuke Nojima
To that effect, this paper considers an existing evolutionary reinforcement learning framework which exploits multi-objective optimization as a way to obtain policies that succeed at behavior-related tasks as well as completing the main goal.
1 code implementation • 2 Mar 2021 • Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi
This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation.