316 citations · 1.3k across the 49 of their papers we have counts for
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Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has…
On fine-tuning of Autoencoders for Fuzzy rule classifiers
Rahul Kumar Sevakula, Nishchal Kumar Verma, Hisao Ishibuchi
Recent discoveries in Deep Neural Networks are allowing researchers to tackle some very complex problems such as image classification and audio classification, with improved theore…
Hypervolume-Optimal -Distributions on Line/Plane-based Pareto Fronts in Three Dimensions
Ke Shang, Hisao Ishibuchi, Weiyu Chen +2
Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with solutions on a Pareto…
Multi-label Classification via Adaptive Resonance Theory-based Clustering
Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo +1
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…
Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Subset selection is an interesting and important topic in the field of evolutionary multi-objective optimization (EMO). Especially, in an EMO algorithm with an unbounded external a…
Niching Diversity Estimation for Multi-modal Multi-objective Optimization
Yiming Peng, Hisao Ishibuchi
Niching is an important and widely used technique in evolutionary multi-objective optimization. Its applications mainly focus on maintaining diversity and avoiding early convergenc…