Markov Blanket Discovery using Minimum Message Length

Li, Yang; Korb, Kevin B.; Allison, Lloyd

doi:10.48550/arxiv.2107.08140

Cited by 4 publications

(3 citation statements)

References 24 publications

(22 reference statements)

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“…Those methods divide features into causal features and non-causal features, with the former having domain invariance and the latter being domain-related, and take feature decoupling as the ultimate goal. Related works include three aspects: 1) Selecting causal feature [34][35] [36]. By learning the Markov Boundary (MB) of the target variable [31], key features are identified.…”

Section: Related Workmentioning

confidence: 99%

Causality-inspired comparative learning supervised model for domain generalization

Zhu,

Yang,

Guo

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Machine learning is good at learning general knowledge and predictive knowledge from known and limited environments, and perform well in similar environments. However, their performance in unknown environments is not satisfactory. Machine learning focus on correlation learning, but correlation is not causality. The non-causal part of the correlation forms spurious correlation that affect the model's generalization ability. Therefore, it is necessary to examine machine learning from a causal perspective. By constructing structural causal models, it is found that the targets of different domains are instrumental variables of each other and can be mutually represented. They have same essential features and different domain features, which lead to causal correlation and spurious correlation with labels respectively. In this paper, a causal-inspired contrastive learning supervised model is designed to strengthen essential features and weaken domain features. With the target of improving the ability of model to capture causal correlations and reducing the interference of spurious correlations, we join transfer and autoencoder with image classification model as a cross-domain contrastive learning model. Experiments show that the proposed framework has a simple and easy-to-implement structure, it performs well on public datasets. By adopting visualization technology, the effects of this method are intuitively demonstrated.

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Section: Related Workmentioning

confidence: 99%

Causality-inspired comparative learning supervised model for domain generalization

Zhu,

Yang,

Guo

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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“…A local structure learning algorithm is introduced for efficient MB discovery in [60] to distinguish the parents from children based on the edge directions from the DAG. A minimum message length-based MBL algorithm is introduced in [61] for large-scale DAG with perfect and imperfect data. The perfect DAG means the detailed information about the states and their relations and is maintained using a conditional probability table.…”

Section: Design Phase Learningmentioning

confidence: 99%

On Distributed Computing Continuum Systems

Dustdar

Pujol

Donta

2023

IEEE Trans. Knowl. Data Eng.

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This article presents our vision on the need of developing new managing technologies to harness distributed "computing continuum" systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed "computing continuum" systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.

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“…Moreover, these CI tests are designed for pair-wise dependence, and can not mine the multivariate relations. Score-based algorithms (Niinimaki and Parviainen 2012;Gao and Ji 2017;Li, Korb, and Allison 2022) adopt score-based structure learning approaches to learn the MB set, where a greedy search method is used to maximize the fitness (scoring function) between the causal graph and training data. Score-based algorithms are far less numerous.…”

Section: Mb and Causal Feature Selection Algorithmsmentioning

confidence: 99%

Practical Markov Boundary Learning without Strong Assumptions

Jiang

et al. 2023

AAAI

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Theoretically, the Markov boundary (MB) is the optimal solution for feature selection. However, existing MB learning algorithms often fail to identify some critical features in real-world feature selection tasks, mainly because the strict assumptions of existing algorithms, on either data distribution, variable types, or correctness of criteria, cannot be satisfied in application scenarios. This paper takes further steps toward opening the door to real-world applications for MB. We contribute in particular to a practical MB learning strategy, which can maintain feasibility and effectiveness in real-world data where variables can be numerical or categorical with linear or nonlinear, pairwise or multivariate relationships. Specifically, the equivalence between MB and the minimal conditional covariance operator (CCO) is investigated, which inspires us to design the objective function based on the predictability evaluation of the mapping variables in a reproducing kernel Hilbert space. Based on this, a kernel MB learning algorithm is proposed, where nonlinear multivariate dependence could be considered without extra requirements on data distribution and variable types. Extensive experiments demonstrate the efficacy of these contributions.

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Markov Blanket Discovery using Minimum Message Length

Cited by 4 publications

References 24 publications

Causality-inspired comparative learning supervised model for domain generalization

Causality-inspired comparative learning supervised model for domain generalization

On Distributed Computing Continuum Systems

Practical Markov Boundary Learning without Strong Assumptions

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