Causality‐based online streaming feature selection

Li, Longzhu; Lin, Yaojin; Zhao, Hong; Chen, Jinkun; Li, Shaozi

doi:10.1002/cpe.6347

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Yu et al [58] formulated the causal feature selection problem with multiple datasets as a search problem and gave the upper and lower bounds of the invariant set, then proposed a multisource feature selection algorithm. Yang et al [55] proposed the concept of N-structures and then designed an MB discovering subroutine to integrate MB learning with N-structures to discover MB while distinguishing direct causes from direct effects. Yu et al [53] proposed a multilabel feature selection algorithm, multi-label feature selection to causal structure learning (M2LC), which learns the causal mechanism behind the data and is able to select causally informative features and visualize common features.…”

Section: Causality-aware Feature Learningmentioning

confidence: 99%

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

et al. 2022

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Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in visual representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.

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Section: Causality-aware Feature Learningmentioning

confidence: 99%

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

et al. 2022

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“…At present, many scholars have conducted in-depth research on the flow feature selection. [25][26][27][28][29] Zhou et al 25 defined an adaptive density neighborhood relation without prior information based on neighborhood rough set, and proposed a new online streaming feature selection algorithm. Li et al 26 proposed a causality-based online streaming feature selection algorithm with neighborhood conditional mutual information.…”

Section: Introductionmentioning

confidence: 99%

“…[25][26][27][28][29] Zhou et al 25 defined an adaptive density neighborhood relation without prior information based on neighborhood rough set, and proposed a new online streaming feature selection algorithm. Li et al 26 proposed a causality-based online streaming feature selection algorithm with neighborhood conditional mutual information. However, these existing online streaming feature selection algorithms ignored the hierarchical relationship between classes, and cannot solve the problem of hierarchical classification.…”

Section: Introductionmentioning

confidence: 99%

“…Transforming the feature selection problem under static and fixed conditions into an online streaming feature selection problem under the concept of flow features plays a key role in dealing with such practical problems. At present, many scholars have conducted in‐depth research on the flow feature selection 25‐29 . Zhou et al 25 defined an adaptive density neighborhood relation without prior information based on neighborhood rough set, and proposed a new online streaming feature selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Online feature selection for hierarchical classification learning based on improved ReliefF

Wang

Ren

et al. 2023

Concurrency and Computation

Self Cite

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In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve the above problems, we propose an online hierarchical feature selection algorithm based on adaptive ReliefF. Firstly, ReliefF is adaptively improved via using the density information of instances around the target sample, making it unnecessary to prespecify parameters. Secondly, the hierarchical relationship between classes is used, and a new method for calculating the feature weight of hierarchical data is defined. Then, an online correlation analysis method based on feature interaction is designed. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between features in order to achieve the dynamic updating of feature redundancy. A large number of experiments verify the effectiveness of the proposed algorithm.

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“…Due to the powerful ability of uncovering the underlying structural knowledge about data generating processes that allows interventions and generalizes well across different tasks and environments, causal reasoning [36][37][38] offers a promising alternative to correlation-learning. Recently, causal reasoning has been attracted increasing attention in a myriad of high-impact domains of computer vision and machine learning, such as interpretable deep learning [39][40][41][42][43][44], causal feature selection [45][46][47][48][49][50][51][52][53][54][55][56][57], visual comprehension [58][59][60][61][62][63][64][65][66][67], visual robustness [68][69][70][71][72][73][74][75], visual question answering [76][77][78][79][80][81], and video understanding…”

Section: Introductionmentioning

confidence: 99%

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Liu¹,

Yushen²,

Yan³

et al. 2022

Preprint

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Spatial-temporal representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal data in big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks an unified guidance and analysis about why modern spatial-temporal representation learning methods are easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for spatialtemporal representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some primary challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in spatialtemporal representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable spatial-temporal representation learning and related real-world applications more efficiently.

show abstract

Causality‐based online streaming feature selection

Cited by 8 publications

References 42 publications

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Online feature selection for hierarchical classification learning based on improved ReliefF

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

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