Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis

Yang, Aijun; Jiang, Xuejun; Shu, Lianjie; Lin, Jin-Guan

doi:10.1007/s00180-016-0665-3

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…In this section, we have introduced the Fisher score [42] filter for feature pre‐selection, that has proven performance in separating relevant features [43]. Compared to other filter approaches like T ‐test, information gain, and Z ‐score, Fisher score filter yields superior results [44]. Nevertheless, each method has its characteristics that affect the stability of the final results.…”

Section: Proposed Methodologymentioning

confidence: 99%

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Baliarsingh

Vipsita

2020

IET syst. biol.

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Section: Proposed Methodologymentioning

confidence: 99%

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Baliarsingh

Vipsita

2020

IET syst. biol.

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“…Let N(L f diam( ), ) denote an -covering. The inequality (47) then yields us where the first inequality follows by using a symmetrization argument that is similar to (Wain-wright, 2019, p. 107), while the second inequality follows from Lemma 6, and the third inequality follows by bounding the covering number by using a volume bound (Akshay, 2016;Yang, 2016;Wainwright, 2019). ◻…”

Section: Lemmamentioning

confidence: 99%

Multi-armed bandits with dependent arms

Singh,

Liu,

Sun

et al. 2023

Mach Learn

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We study a variant of the multi-armed bandit problem (MABP) which we call as MABs with dependent arms. Multiple arms are grouped together to form a cluster, and the reward distributions of arms in the same cluster are known functions of an unknown parameter that is a characteristic of the cluster. Thus, pulling an arm i not only reveals information about its own reward distribution, but also about all arms belonging to the same cluster. This "correlation" among the arms complicates the exploration-exploitation trade-off that is encountered in the MABP because the observation dependencies allow us to test simultaneously multiple hypotheses regarding the optimality of an arm. We develop learning algorithms based on the principle of optimism in the face of uncertainty (Lattimore and Szepesvári in Bandit algorithms, Cambridge University Press, 2020), which know the clusters, and hence utilize these additional side observations appropriately while performing exploration-exploitation trade-off. We show that the regret of our algorithms grows as O(K log T) , where K is the number of clusters. In contrast, for an algorithm such as the vanilla UCB that does not utilize these dependencies, the regret scales as O(M log T) , where M is the number of arms. When K ≪ M , i.e. there is a lot of dependencies among arms, our proposed algorithm drastically reduces the dependence of regret on the number of arms.

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“…Since treatment therapy and medical knowledge rapidly progresses, it is difficult for a clinician to possess current development and knowledge in medical settings [2]. Alternatively, with the emergence of computing technology, now it is relatively easy to store and acquire lots of data digitally, for example in devoted dataset of electronic patient records [3]. Intrinsically, the positioning of computerized medicinal decision support system (DSS) becomes a feasible method for helping physicians to accurately and swiftly identify individual patients [4].…”

Section: Introductionmentioning

confidence: 99%

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Alshehri¹,

Alamgeer²,

Hilal³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

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Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis

Cited by 8 publications

References 44 publications

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Multi-armed bandits with dependent arms

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

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