A novel fuzzy ARTMAP with area of influence

Matias, Alan L. S.; Neto, Ajalmar R. da Rocha; Mattos, César Lincoln C.; Gomes, João P. P.

doi:10.1016/j.neucom.2020.11.053

Cited by 9 publications

(13 citation statements)

References 26 publications

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“…In general, ART-based clustering algorithms show superior clustering performance than GNGbased and SOINN-based algorithms [11], [12]. Moreover, because ART-based clustering algorithms can theoretically realize sequential and class-incremental learning without catastrophic forgetting, a number of ART-based clustering algorithms and their improvements have been proposed in both supervised learning [22]- [24] and unsupervised learning [7], [8], [25], [26]. One common drawback of ART-based clustering algorithms that they need to specify a similarity threshold (i.e., a vigilance parameter).…”

Section: Literature Review a Clustering Algorithms Capable Of Continu...mentioning

confidence: 99%

Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

Masuyama,

Amako,

Yamada

et al. 2022

Preprint

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Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. 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. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

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Section: Literature Review a Clustering Algorithms Capable Of Continu...mentioning

confidence: 99%

Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

Masuyama,

Amako,

Yamada

et al. 2022

Preprint

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“…In contrast to GNG-based algorithms, ART-based algorithms can theoretically avoid the plasticity-stability dilemma by utilizing a predefined similarity threshold (i.e., a vigilance parameter) for controlling a learning process. Thanks to this ability, a number of ART-based algorithms and their improvements have been proposed for both supervised learning [22]- [24] and unsupervised learning [25]- [28]. Specifically, algorithms which utilize the CIM as a similarity measure have achieved faster and more stable self-organizing performance than GNG-based algorithms [14], [15], [29], [30].…”

Section: Literature Review a Growing Self-organizing Clustering Algor...mentioning

confidence: 99%

“…One drawback of the ART-based algorithms is a specification of data-dependent parameters such as a similarity threshold. Several studies have proposed to solve this drawback by utilizing multiple vigilance levels [31], adjusting parameters during a learning process [24], and estimating parameters from given data [16]. In particular, CAEA [16], which utilizes the CIM as a similarity measure, has shown superior clustering performance while successfully reducing the effect of data-dependent parameters.…”

Section: Literature Review a Growing Self-organizing Clustering Algor...mentioning

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.

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“…A successful approach to avoid the plasticity-stability dilemma is the ART-based algorithms [22]. Because the ARTbased algorithms realize sequential and class-incremental learning without the catastrophic forgetting, a number of the ART-based algorithms and their improvements are proposed in both supervised learning [23][24][25] and unsupervised learning [26][27][28][29]. In the ART-based algorithms, a criterion of a new category (node) generation, i.e., a similarity measurement between a node and an instance, has a great impact on the classification/clustering performance.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…Compute a posterior probability P (H|E) by (24). Determine a predicted label vector l * by (25). return l * .…”

Section: Outputmentioning

confidence: 99%

Multi-label Classification Based on Adaptive Resonance Theory

Masuyama

Nojima

Ishibuchi

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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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. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multilabel datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.

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A novel fuzzy ARTMAP with area of influence

Cited by 9 publications

References 26 publications

Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

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

Multi-label Classification Based on Adaptive Resonance Theory

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