An affine invariant <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si10.gif" display="inline" overflow="scroll"><mml:mi>k</mml:mi></mml:math>-nearest neighbor regression estimate

Biau, Gérard; Devroye, Luc; Dujmović, Vida; Krzyak, Adam

doi:10.1016/j.jmva.2012.05.020

Cited by 49 publications

References 34 publications

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Wind turbine condition monitoring based on double‐layer ensemble KNN method

Zhang,

Huang,

Liu

et al. 2023

Energy Science & Engineering

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An ensemble K‐nearest neighbor model based on the double‐layer sampling method was proposed for the condition monitoring of wind turbine (WT) gearboxes. The distance function was improved to affinity distance, which helped overcome the limitation of obtaining local optimums. The feature priority was calculated based on the regularized mutual information, and a double‐layer sampling method combining data and feature sampling was designed. On the basis of the statistical process control technology, the warning threshold was designed and the real‐time residual was analyzed. The condition of the gearbox was monitored by combining the failure rate. Experimental results with real supervisory control and data acquisition (SCADA) data demonstrated that the proposed method improved the estimation accuracy of the model and could realize early fault warnings of a WT gearbox approximately 20 days earlier than the SCADA system.

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Wind turbine condition monitoring based on double‐layer ensemble KNN method

Zhang,

Huang,

Liu

et al. 2023

Energy Science & Engineering

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An Object‐Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes

Zhao

Bolouri

Zhao

et al. 2016

Genetic Epidemiology

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Recent genomewide association studies confirm that HLA genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as “objects” or “exemplars”, one focuses on systemic associations of disease phenotype with “objects” via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1 and –DPB1), to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set indicating that this methodology is useful to build predictive models with complex HLA genotypes.

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Order statistics and nearest neighbors

Biau

Devroye

2015

Lectures on the Nearest Neighbor Method

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An affine invariant k-nearest neighbor regression estimate

Cited by 49 publications

References 34 publications

Wind turbine condition monitoring based on double‐layer ensemble KNN method

Wind turbine condition monitoring based on double‐layer ensemble KNN method

An Object‐Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes

Order statistics and nearest neighbors

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