Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

Sun, Jianyuan; Yu, Hui; Zhong, Guoqiang; Dong, Junyu; Shu, Zhang; Yu, Hongchuan

doi:10.1109/tcyb.2020.2972956

Cited by 31 publications

(13 citation statements)

References 46 publications

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“…Then the final assigned value i (N , v) is Shapley value. It is proved that Shapley value is the only solution that satisfies the above four conditions [29,30] and is calculated by the following formula:…”

Section: Task Offloading Weight Modelmentioning

confidence: 99%

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Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

Liu

2021

The Journal of Engineering

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This paper proposes a task offloading and resource allocation algorithm based on mobile edge computing. Firstly, for the long-term performance optimization of small base stations, the system model is established according to task arrival characteristics, credit relationship between small base stations, time delay and energy consumption of computing tasks and cable channel congestion. Secondly, the energy consumption deficit queue based on Lyapunov drift penalty technology used for the energy consumption constraint of small base stations in long-term optimization process. The energy consumption deficit queue is established for each small base station to couple the energy consumption and time of small base stations, so that small base stations can meet the energy consumption constraints in longterm optimization process. Finally, game theory is introduced to calculate offloading weight by the offloading weight model based on Shapley value. Besides, the offloading weight is calculated equitably according to the return of different tasks. Simulation results on MAT-LAB platform show that the proposed algorithm can achieve Nash equilibrium after finite iterations. Moreover, its performance on energy consumption, time delay and number of tasks successfully offloaded is better than other comparison strategies.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Task Offloading Weight Modelmentioning

confidence: 99%

“…It is proved that Shapley value is the only solution that satisfies the above four conditions [29, 30] and is calculated by the following formula:

ψ_{i} (N, v) = \frac{1}{N!} \sum_{S \subseteq N \{i\}} |S|! (|N| - |S| - 1)! [v (S \cup \{i\} - v (S)]

…”

Section: Task Offloading and Resource Allocation Algorithm Based On Mecmentioning

confidence: 99%

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

Liu

2021

The Journal of Engineering

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“…Those lncRNAs were considered as candidate variables if the p-value was less than 0.001. Subsequently, the Random Survival Forest (RSF) method [14] was used to select a smaller number of lncRNAs further to construct the Cox regression analyzed models. The risk index was calculated based on the expression of lncRNAs and coe cients obtained from the multivariate Cox model.…”

Section: Construction Of the Prognostic Lncrna Signaturementioning

confidence: 99%

The Construction and Analysis of a Novel Four-Long Non-Coding RNA Signature Predicting Survival of Breast Cancer Patients

Qin¹,

Feng²,

Yu³

et al. 2021

Preprint

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Background As the critical regulators for tumorigenesis and progression, long-noncoding RNAs (lncRNAs) are becoming novel prognostic biomarkers for tumor patients. By the levels of lncRNAs expression, the patients with breast carcinoma may be divided into subgroups with different risk scores. Nevertheless, there is limited evidence to evaluate the role of lncRNAs in the prognosis of breast carcinoma. The present study aimed to construct lncRNA signatures for prognostic analysis and assist clinicians in choosing optimal therapies. Methods Abnormal expression profiles of breast cancer-associated lncRNAs were analyzed based on the TCGA datasets. Univariate and multivariate Cox regression analysis was used to build a prognostic risk signature according to the lncRNAs expression. The prognostic ability of this signature was verified in various subgroups. Functional enrichment analysis was employed to reveal the potential roles of these predictive lncRNAs in cancer-related biological processes and pathways. Results Compared with normal breast tissues, the differential analysis demonstrated that 286 lncRNAs were abnormally expressed in breast carcinoma. A four-lncRNA signature (RP1-193H18.2, AL022341.3, WDR86-AS1, LINC00511) was found to be closely related to the prognosis of breast carcinoma. The four-lncRNA signature could also qualify the magnitude of treatment benefits for different breast cancer subtypes. Additionally, it was an independent risk factor out of other clinicopathological parameters based on the multivariate Cox analysis. We also uncovered that the four predictive lncRNAs are involved in multiple cellular progression and pathways of breast cancer. Conclusions The four-lncRNA signature could be an essential reference for prognostic prediction and making therapeutic strategies.

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“…These trees can be used to model the response variables through recursive partition and predict the final results jointly [16]. The random forest algorithm is commonly employed in data classification and regression [17]. At present, there are three mainstream decision tree algorithms, including ID3, C4.5 and CART.…”

Section: Random Forestmentioning

confidence: 99%

Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

Tang

Yang³

et al. 2020

Preprint

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Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

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Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

Cited by 31 publications

References 46 publications

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

The Construction and Analysis of a Novel Four-Long Non-Coding RNA Signature Predicting Survival of Breast Cancer Patients

Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

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Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

Cited by 31 publications

References 46 publications

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

The Construction and Analysis of a Novel Four-Long Non-Coding&nbsp;RNA Signature Predicting Survival of Breast Cancer Patients

Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

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The Construction and Analysis of a Novel Four-Long Non-Coding RNA Signature Predicting Survival of Breast Cancer Patients