A Semi-Supervised Self-Organizing Map for Clustering and Classification

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

doi:10.1109/ijcnn.2018.8489675

Cited by 13 publications

(23 citation statements)

References 17 publications

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“…Semi-supervised learning is catching on quickly among developers and researchers, as the numbers of unlabelled data are bigger than labeled data in a dataset and this issue is growing speedily [36,37]. Basically, semisupervised learning is trained on a combination of labeled and unlabeled data.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Section: Machine Learning Algorithmmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…In ALTSS-SOM, each node j in the map represents a cluster and is associated with four m-dimensional vectors, where m is the number of input dimensions: The first three vectors, c j , ω j , and δ j , are the same as defined in [2], , where c j = {c ji , i = 1, · · ·, m} is the center vector; ω j = {ω ji , i = 1, · · ·, m} is the relevance vector; δ j = {δ ji , i = 1, · · ·, m} is the distance vector that stores moving averages of the observed distance between the input patterns x and the center vector |x − c j (n)| for each dimension. Note, however, that δ in SS-SOM and ALTSS-SOM can be seen as the biased first moment estimate.…”

Section: A Structure Of the Nodesmentioning

confidence: 99%

“…We point out that prototype-based methods have been successfully applied for both tasks. Methods based on Self-Organizing Maps [2], [5], [6] and K-Means [7] can be highlighted as examples, as well as deep learning techniques [8]- [10]. The Self-Organizing Map (SOM) is an unsupervised learning method, frequently applied for clustering, while Learning Vector Quantization (LVQ) [5], its supervised counterpart that shares many similarities, is normally used for classification.…”

Section: Introductionmentioning

confidence: 99%

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

Braga

Bassani

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semisupervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values.

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“…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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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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A Semi-Supervised Self-Organizing Map for Clustering and Classification

Cited by 13 publications

References 17 publications

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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

Deep Categorization with Semi-Supervised Self-Organizing Maps

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