Efficient class incremental learning for multi-label classification of evolving data streams

Shi, Zhongwei; Xue, Yun; Wen, Yonggang; Cai, Guoyong

doi:10.1109/ijcnn.2014.6889926

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…The improved version, ADWIN2, is more efficient than the first one in time and memory consumption [31]. Other research such as [32,33] use two adjustable windows to represent old and new samples. Xioufis et al [9] used two windows per label to capture the positive and negative groups of samples.…”

Section: Proposed Methods 31 the Online Clustering Modelmentioning

confidence: 99%

“…That framework was also combined with ADWIN [31] to form a new algorithm named EaHTps which can handle concept drift. The Pruned Set-based label combination module of EaHTps was improved by Shi et al [32] in which the new frequent label combinations are dynamically recognized to update the set of label combinations. Xioufis et al [9] used BR to solve the MLC by transforming the multi-label task into several binary classification tasks.…”

Section: Multi-label Classification For Data Streammentioning

confidence: 99%

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Multi-label classification via incremental clustering on an evolving data stream

Nguyen

Dang

Luong

et al. 2019

Pattern Recognition

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With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.

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Section: Proposed Methods 31 the Online Clustering Modelmentioning

confidence: 99%

Section: Multi-label Classification For Data Streammentioning

confidence: 99%

Multi-label classification via incremental clustering on an evolving data stream

Nguyen

Dang

Luong

et al. 2019

Pattern Recognition

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“…Some approaches have been proposed for class-incremental learning (Da et al 2014;Shi et al 2014) and stream multi-label learning (Mu et al 2017;Qu et al 2009;Read et al 2011;. In these two problems, new labels are unobserved during the training stage, but appear in the test stage.…”

Section: Related Workmentioning

confidence: 99%

Multi-label learning with missing and completely unobserved labels

Huang

Qian

et al. 2021

Data Min Knowl Disc

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Multi-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved for the training data. We refer to the problem as multi-label learning with missing and completely unobserved labels, and argue that it is necessary to discover these completely unobserved labels in order to mine useful knowledge and make a deeper understanding of what is behind the data. In this paper, we propose a new approach named MCUL to solve multi-label learning with Missing and Completely Unobserved Labels. We try to discover the unobserved labels of a multi-label data set with a clustering based regularization term and describe the semantic meanings of them based on the label-specific features learned by MCUL, and overcome the problem of missing labels by exploiting label correlations. The proposed method MCUL can predict both the observed and newly discovered labels simultaneously for unseen data examples. Experimental results validated over ten benchmark datasets demonstrate that the proposed method can outperform other state-of-the-art approaches on observed labels and obtain an acceptable performance on the new discovered labels as well.

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“…Moreover, it is also able to simulate how this dependence and relationship evolve with the change of time. As far as we know, the most often used approach, in [115], [116], [121], is the MOA (Massive Online Analysis) framework, on which we could synthesize the needing dataset with different concept drifts and label dependencies.…”

Section: Synthetic Datasetsmentioning

confidence: 99%

A Survey on Multi-Label Data Stream Classification

Xiu-lin

Zhang

et al. 2020

IEEE Access

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Nowadays, many real-world applications of our daily life generate massive volume of streaming data at a higher speed than ever before, to name a few, Web clicking data streams, sensor network data and credit transaction streams. Contrary to traditional data mining using static datasets, there are several challenges for data stream mining, for instance, finite memory, one-pass and timely reaction. In this survey, we provide a comprehensive review of existing multi-label streams mining algorithms and categorize these methods based on different perspectives, which mainly focus on the multi-label data stream classification. We first briefly summarize existing multi-label and data stream classification algorithms and discuss their merits and demerits. Secondly, we identify mining constraints on classification for multi-label streaming data, and present a comprehensive study in algorithms for multi-label data stream classification. Finally, several challenges and open issues in multi-label data stream classification are discussed, which are worthwhile to be pursued by the researchers in the future. INDEX TERMS Data stream mining, multi-label data, multi-label classification.

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Efficient class incremental learning for multi-label classification of evolving data streams

Cited by 12 publications

References 10 publications

Multi-label classification via incremental clustering on an evolving data stream

Multi-label classification via incremental clustering on an evolving data stream

Multi-label learning with missing and completely unobserved labels

A Survey on Multi-Label Data Stream Classification

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