Online Semi-supervised Growing Neural Gas for Multi-label Data Classification

Boulbazine, Samira; Cabanès, Guénaël; Matei, Basarab; Bennani, Younès

doi:10.1109/ijcnn.2018.8489776

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…[14,15] presented online multilabel algorithms in which the dataset is divided into many single-label datasets to solve the multi-label classification problem. The online multi-label semisupervised (OMLSS) [16] introduced non-local labeling functions taking into account the topology of the network in the prediction of a label and improving the influence of the labeling strategy on the topology of the network in the multi-label case, using the labels to improve the synaptic links.…”

Section: Related Work 1online Learningmentioning

confidence: 99%

Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

ZHANG

2024

Wuhan Univ. J. Nat. Sci.

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Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification. Although this technique is widely used in multi-label classification problems, batch learning deals with most issues, which consumes a lot of time and space resources. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets. However, existing online learning research has done little to consider correlations between labels. On the basis of existing research, this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples. We evaluate the performance of the proposed algorithm on several public datasets. Experiments show the effectiveness of our algorithm.

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Section: Related Work 1online Learningmentioning

confidence: 99%

Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

ZHANG

2024

Wuhan Univ. J. Nat. Sci.

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“…Online learning and class-incremental learning: Online learning is defined as follows. The training data is input in a sequence, and the model parameters are updated online to provide better results for new test samples after each iteration [21][22][23][24][25]. Class-incremental learning can be used to segment training data into sequences according to tasks, process the tasks in order, incrementally learn a classification model, and then effectively classify any new labels in task sequence [26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

“…Many kinds of researches have been proposed in the past few decades to solve the problem of new labels, such as online learning [21][22][23][24][25], class incremental learning [26][27][28][29], zero-shot learning [30][31][32][33], Generalized out-of-distribution detection [34][35][36][37][38][39][40][41][42][43][44][45], etc. In the zero-shot learning problem, the information of new labels is usually known during the training stage, including the semantic information and their total numbers.…”

Section: Introductionmentioning

confidence: 99%

Discovering Unknown Labels for Multi-Label Image Classification

Huang

Yan

Zheng

2022

Preprint

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A multi-label learning (MLL) method can simultaneously process the instances with multiple labels, and many well-known methods have been proposed to solve various MLL-related problems. The existing MLL methods are mainly applied under the assumption of a fixed label set, i.e., the class labels are all observed for the training data. However, in many real-world applications, there may be some unknown labels outside of this set, especially for large-scale and complex datasets. In this paper, a multi-label classification model based on deep learning is proposed to discover the unknown labels for multi-label image classification. It can simultaneously predict known and unknown labels for unseen images. Besides, an attention mechanism is introduced into the model, where the attention maps of unknown labels can be used to observe the corresponding objects of an image and to get the semantic information of these unknown labels.

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“…The learning process for larger data sets is more agile than other commonly used cluster examination techniques. It does not require significant RAM resources (Migdał-Najman and Najman 2013; Boulbazine et al 2018).…”

Section: Artificial Neural Network Type Gngmentioning

confidence: 99%

The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers

Migdał-Najman

Badowska

2020

Adv Data Anal Classif

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The paper sheds light on the use of a self-learning GNG neural network for identification and exploration of the purchasing behaviour patterns. The test has been conducted on the data collected from consumers aged 60 years and over, with regard to three product purchases. The primary data used to explore the purchasing behaviour patterns was collected during a survey carried out among the elderly students at the Universities of Third Age in Slovenia, the Czech Republic and Poland, in the years 2017-2018. Finally, a total of six different types of purchasing patterns have been identified, namely the 'thoughtful decision', the 'sensitive to recommendation', the 'beneficiary, the 'short thoughtful decision', the 'habitual decision' and 'multiple' patterns. The most significant differences in the purchasing patterns of the three national samples have been identified with regard to the process of purchasing a smartphone, while the most repetitive patterns have been identified with regard to the purchasing of a new product. The results significantly support the GNG network's validity for identification of consumer behaviour patterns. The application of this method allowed quick and effective to identify and segment consumers groups as well as facilitated the mapping of the differences among these groups and to compare the consumption behaviour expressed by consumers on different markets. The identified consumer purchase patterns may play a basic role for marketers to understand consumer behaviour and then propose tailored strategies in international marketing.

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Online Semi-supervised Growing Neural Gas for Multi-label Data Classification

Cited by 10 publications

References 24 publications

Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

Discovering Unknown Labels for Multi-Label Image Classification

The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers

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