10 papers
PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering
Naoki Masuyama, Yusuke Nojima, Stefan Wermter +3
Online clustering methods that adaptively create and update nodes as data arrive often make node learning explicit, whereas the mapping from the learned node state to output cluste…
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima +3
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 cluster…
An Efficient Evolutionary Algorithm for Few-for-Many Optimization
Ke Shang, Hisao Ishibuchi, Zexuan Zhu +1
Few-for-many (F4M) optimization, recently introduced as a novel paradigm in multi-objective optimization, aims to find a small set of solutions that effectively handle a large numb…
An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
Naoki Masuyama, Yuichiro Toda, Yusuke Nojima +1
Clustering in stationary and nonstationary settings, where data distributions remain static or evolve over time, requires models that can adapt to distributional shifts while prese…
Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama +3
Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs), a family of rule-based m…
A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems
Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata
The decision-making process significantly influences the predictions of machine learning models. This is especially important in rule-based systems such as Learning Fuzzy-Classifie…