Multi-label Classification Based on Adaptive Resonance Theory

Masuyama, Naoki; Nojima, Yusuke; Ishibuchi, Chu Kiong LooHisao

doi:10.1109/ssci47803.2020.9308356

Cited by 3 publications

(4 citation statements)

References 57 publications

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“…In this paper, two modifications of the CIM computation [48] are integrated into CAEAC in order to mitigate the above-mentioned effects: (1) One is to compute the CIM by using each individual attribute separately, and the average CIM value is used for similarity measurement, and (2) the other is to apply a clustering algorithm to attribute values, then attributes with similar value ranges are grouped. The CIM is computed by using each attribute group, and the average CIM value is used for similarity measurement.…”

Section: Modifications Of the Cim Computationmentioning

confidence: 99%

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Class-Wise Classifier Design Capable of Continual Learning Using Adaptive Resonance Theory-Based Topological Clustering

Masuyama,

Nojima,

Dawood

et al. 2023

Applied Sciences

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This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.

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Section: Modifications Of the Cim Computationmentioning

confidence: 99%

“…In this approach, for every λ data points, the clustering algorithm presented in [48] is applied to the attribute values. Each attribute value of λ data points is regarded as a onedimensional vector and used as an input to the clustering algorithm.…”

Section: Clustering-based Approachmentioning

confidence: 99%

Class-Wise Classifier Design Capable of Continual Learning Using Adaptive Resonance Theory-Based Topological Clustering

Masuyama,

Nojima,

Dawood

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, two modifications of the CIM computation [41] are integrated into CAEAC in order to mitigate the above-mentioned effects: 1) one is to compute the CIM by using each individual attribute separately, and the average CIM value is used for similarity measurement, and 2) the other is to apply a clustering algorithm to attribute values, then attributes with similar value ranges are grouped. The CIM is computed by using each attribute group, and the average CIM value is used for similarity measurement.…”

Section: Modifications Of the Cim Computationmentioning

confidence: 99%

“…In this approach, for every λ data points, the clustering algorithm presented in [41] is applied to the attribute values. Each attribute value of λ data points is regarded as a onedimensional vector and used as an input to the clustering algorithm.…”

Section: ) Clustering-based Approachmentioning

confidence: 99%

Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering

Masuyama¹,

Tsubota²,

Nojima³

et al. 2022

Preprint

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This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has the superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.

show abstract