A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Braga, Pedro H. M.; Bassani, Hansenclever F.

doi:10.1109/ijcnn.2019.8851839

Cited by 3 publications

(3 citation statements)

References 25 publications

(62 reference statements)

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“…In the Breast dataset, it achieved the same value as other clustering methods. In Diabetes and Vowel, it was statistically equal to LARFDS-SOM [8] / SS-SOM [15] and ALT-SSSOM [17], respectively, because in the unlabeled scenario, it behaves similarly. In the Glass, Liver, and Shape datasets, the batch size has a slightly negative influence on the outcome, once it presented a small degradation in terms of performance, which is an effect of the mean vector update rule.…”

Section: Batch Ss-som On Uci Datasetsmentioning

confidence: 94%

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Deep Categorization with Semi-Supervised Self-Organizing Maps

Braga

Medeiros

Bassani

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning. However, it is expected that they become far more important in the longer term. This article presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances that came with the rise of Deep Learning, such as batch training. The results show that Batch SS-SOM is a good option for semisupervised classification and clustering. It performs well in terms of accuracy and clustering error, even with a small number of labeled samples, as well as when presented to unsupervised data, and shows competitive results in transfer learning scenarios in traditional image classification benchmark datasets.

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Section: Batch Ss-som On Uci Datasetsmentioning

confidence: 94%

“…However, as mentioned, conventional forms of clustering suffer when dealing with highdimensional spaces. In this sense, SOM-based algorithms have been proposed [8], [15]- [17]. However, most of them do not have any form to explore the benefits of more advanced techniques, even a simple form of mini-batch learning.…”

Section: Introductionmentioning

confidence: 99%

Deep Categorization with Semi-Supervised Self-Organizing Maps

Braga

Medeiros

Bassani

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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“…Each map unit is associated with a prototype vector from the original data space. State-of-the-art SOMbased models are suitable for clustering high-level features, such Braga & Bassani (2019); Bassani & Araujo (2015).…”

Section: Research Problem and Motivationmentioning

confidence: 99%

Deep Clustering Self-Organizing Maps with Relevance Learning

Braga,

Rapela,

Bassani

2020

LatinX in AI at International Conference on Machine Learning 2020

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Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning. However, it is expected that they become far more important in the longer term. This article presents a semi-supervised model, called Batch SemiSupervised Self-Organizing Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances that came with the rise of Deep Learning, such as batch training. The results show that Batch SS-SOM is a good option for semisupervised classification and clustering. It performs well in terms of accuracy and clustering error, even with a small number of labeled samples, as well as when presented to unsupervised data, and shows competitive results in transfer learning scenarios in traditional image classification benchmark datasets.

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A3SOM, abstained explainable semi-supervised neural network based on self-organizing map

Creux,

Zehraoui,

Hanczar

et al. 2023

PLoS ONE

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In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with data. In this context, we propose A3SOM, an abstained explainable semi-supervised neural network that associates a self-organizing map to dense layers in order to classify samples. Abstained classification enables the detection of new classes and class overlaps. The use of a self-organizing map in A3SOM allows integrated visualization and makes the model explainable. Along with describing our approach, this paper shows that the method is competitive with other classifiers and demonstrates the benefits of including abstention rules. A use case is presented on breast cancer subtype classification and discovery to show the relevance of our method in real-world medical problems.

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A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Cited by 3 publications

References 25 publications

Deep Categorization with Semi-Supervised Self-Organizing Maps

Deep Categorization with Semi-Supervised Self-Organizing Maps

Deep Clustering Self-Organizing Maps with Relevance Learning

A3SOM, abstained explainable semi-supervised neural network based on self-organizing map

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