Multi-label classification via label correlation and first order feature dependance in a data stream

Nguyen, Tien Thanh; Nguyen, Thị Thu Hien; Luong, Anh Vu; Nguyen, Quoc Viet Hung; Liew, Alan Wee‐Chung; Stantić, Bela

doi:10.1016/j.patcog.2019.01.007

Cited by 45 publications

(17 citation statements)

References 56 publications

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“…Many studies are carried out about the analysis and prediction methods of the technical and economic data [21][22][23]. Nguyen et al [24] analyzed the prediction and interpolation methods of missing economic data of the mining enterprise, such as the mean method, the weighted average method, the linear regression method, the maximum expected method, and the multiple imputation method. Muthukrishna et al [25] collected the borehole data and discovered the global trend and the aeolotropism existing in the data.…”

Section: Introductionmentioning

confidence: 99%

Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

Duan

Gao

2021

Security and Communication Networks

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Massive English film and television resources will provide people with a rich content, while it has become more and more difficult for people to find the resources and information they need. Through the analysis of massive amounts of film and television data, the application system can effectively push the works that users may like. In response to this problem, we can build an English film and television resource information mining model by combining fuzzy neural network algorithms and dynamic data stream classification technology. Firstly, we use dynamic data stream classification technology to preprocess and screen English film and television resource information. Secondly, we use the fuzzy neural network algorithm to conduct data mining on related film and television resource information. The experimental results verify the superior performance of the English film and television resource information mining model established in this paper. This model can help people to find the resources and information they need.

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Section: Introductionmentioning

confidence: 99%

Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

Duan

Gao

2021

Security and Communication Networks

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“…And in [107], an approach based on Bayesian was proposed for multi-label streaming data classification, which takes into account not only the correlations between pairs of labels but also the relations between label and feature. In this model, the label correlation is captured by learning with arrived data instances with true labels, meanwhile, the number of predicted labels is adaptive according to the Hoeffding inequality as well as the label cardinality.…”

Section: ) Aa Based Multi-label Data Stream Classification Methodsmentioning

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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“…Hence, the ECC method is proposed in which several CC classifiers are generated with random orders over the label space [18]. Several methods have been introduced to improve CC's effectiveness, such as replacing binary values by probabilistic outputs [40], finding a proper chain sequence by Monte Carlo method [41], and using recurrent neural network focusing only on positive labels as an extension of probabilistic CC approach [42]. Another popular approach, which considers label correlations, is the Label Power set (LP), in which all different combinations of labels are considered as classes under the single-class problem [43].…”

Section: Related Workmentioning

confidence: 99%

HML-RF: Hybrid Multi-Label Random Forest

2022

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Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role. Its always been a question of research to decide the order of labels to exploit their inter-dependency. Hence, to this end, many research works are done that, in general, can be categorized as problem transformation and algorithm adaptation techniques. The problem transformation reconstructs the multi-label problem as a multiple single class problem. The algorithm transformation modifies the existing well-known machine learning approaches to solve the multi-label classification problem. However, these two techniques have their pros and cons. In this paper, we propose a novel approach to consider the merits of both techniques, hence named Hybrid Multi-Label Random Forest (HML-RF). The multi-label decision trees are used as base classifiers in the proposed approach to construct the HML-RF model. Each base classifier is constructed over a randomly selected subset of labels to exploit the label dependency. We also formulate a way to compute the tree strength of a multi-label decision tree, which is used to construct the HML-RF with strength (HML-RFws). The efficacy of the proposed approach is tested over the ten wellknown and publicly available datasets. Experimental results show the HML-RF is performing better for at-least six datasets, and the HML-RFws is performing better for at-least nine datasets in comparison to state-of-the-art approaches in terms of accuracy, hamming loss, and zero-one loss. Finally, the statistical test is also validating all the experimental results.

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Multi-label classification via label correlation and first order feature dependance in a data stream

Abstract: label classification via labels correlation and one-dependence features on data stream,

Cited by 45 publications

References 56 publications

Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

A Survey on Multi-Label Data Stream Classification

HML-RF: Hybrid Multi-Label Random Forest

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