Online Multi-Label Streaming Feature Selection With Label Correlation

You, Dianlong; Wang, Yang; Xiao, Jiawei; Lin, Yaojin; Pan, Maosheng; Chen, Zhen; Shen, Limin; Wu, Xindong

doi:10.1109/tkde.2021.3113514

Cited by 29 publications

(4 citation statements)

References 48 publications

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“…In [17], a feature selection scheme is developed specifically for the multi-label streaming problem space. It leverages correlation degrees, relevance analysis, and redundancy analysis to derive the best feature sets for correlated labels.…”

Section: Data Streamsmentioning

confidence: 99%

Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams

2022

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Section: Data Streamsmentioning

confidence: 99%

Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams

2022

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“…Moreover, the online feature selection methods receive features one by one dynamically. These methods include grafting [8], Alpha-investing (α− investing) [8], Scalable and Accurate Online Feature Selection (SAOLA) [20], and Online Streaming Feature Selection (OSFS) [21]. Grafting was the first algorithm designed by Perkins and Theiler.…”

Section: Related Workmentioning

confidence: 99%

“…It fails to find an optimum relevance threshold value to remove all redundant features. In contrast, OSFS [21] removes unnecessary features which are not relevant/associated with the target feature, T, using conditional independence. It uses two steps to achieve the Parents-Child relevant to the target feature T. The first step analyzes the online relevance, and the second step analyzes online redundancy.…”

Section: Related Workmentioning

confidence: 99%

Online Streaming Features Selection via Markov Blanket

et al. 2022

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Streaming feature selection has always been an excellent method for selecting the relevant subset of features from high-dimensional data and overcoming learning complexity. However, little attention is paid to online feature selection through the Markov Blanket (MB). Several studies based on traditional MB learning presented low prediction accuracy and used fewer datasets as the number of conditional independence tests is high and consumes more time. This paper presents a novel algorithm called Online Feature Selection Via Markov Blanket (OFSVMB) based on a statistical conditional independence test offering high accuracy and less computation time. It reduces the number of conditional independence tests and incorporates the online relevance and redundant analysis to check the relevancy between the upcoming feature and target variable T, discard the redundant features from Parents-Child (PC) and Spouses (SP) online, and find PC and SP simultaneously. The performance OFSVMB is compared with traditional MB learning algorithms including IAMB, STMB, HITON-MB, BAMB, and EEMB, and Streaming feature selection algorithms including OSFS, Alpha-investing, and SAOLA on 9 benchmark Bayesian Network (BN) datasets and 14 real-world datasets. For the performance evaluation, F1, precision, and recall measures are used with a significant level of 0.01 and 0.05 on benchmark BN and real-world datasets, including 12 classifiers keeping a significant level of 0.01. On benchmark BN datasets with 500 and 5000 sample sizes, OFSVMB achieved significant accuracy than IAMB, STMB, HITON-MB, BAMB, and EEMB in terms of F1, precision, recall, and running faster. It finds more accurate MB regardless of the size of the features set. In contrast, OFSVMB offers substantial improvements based on mean prediction accuracy regarding 12 classifiers with small and large sample sizes on real-world datasets than OSFS, Alpha-investing, and SAOLA but slower than OSFS, Alpha-investing, and SAOLA because these algorithms only find the PC set but not SP. Furthermore, the sensitivity analysis shows that OFSVMB is more accurate in selecting the optimal features.

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“…[33][34][35] For multi-label data, Liu et al 36 combined online group selection and online inter-group selection, and designed a criterion in group selection to select feature groups that are important to the label set. You et al 37 developed a new online multi-label streaming feature selection scheme that considers label correlation. However, the above algorithms ignore the differences of features in each category, and the calculation of feature importance should consider the categories.…”

Section: Introductionmentioning

confidence: 99%

Online streaming feature selection based on hierarchical structure information

Lin,

Wang,

et al. 2024

Concurrency and Computation

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SummaryHierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction accuracy of classification models. To present, existing online feature selection methods ignore the hierarchical structure of data. In addition, the dependency relationships in the hierarchical structure can serve as auxiliary knowledge to aid feature selection. Based on this, this paper proposes an online streaming hierarchical feature selection method based on kernelized fuzzy rough sets (OFS‐HNFRS). First, we use the prior knowledge of the hierarchical structure to divide the sample set into multiple subsets. Second, the dependency relationship of hierarchical structure is extended to kernelized fuzzy rough sets, and hierarchical category dependency based on kernelized fuzzy rough sets is defined. Finally, a new online feature selection framework is proposed, which is used to evaluate the relevance, significance, and redundancy of features. We verify the effectiveness of the proposed algorithm on six hierarchical datasets and eight flat datasets.

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Online Multi-Label Streaming Feature Selection With Label Correlation

Cited by 29 publications

References 48 publications

Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams

Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams

Online Streaming Features Selection via Markov Blanket

Online streaming feature selection based on hierarchical structure information

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