Variable Screening of Neural Network based on MIV

Screening variables with significant features as the input data of network, is an important step in application of neural network to predict and analysis problems. This paper proposed a method using MIV algorithm to screen variables of BP neural network.And experimental results show that, the proposed technique is practical and reliable.

The abstract of doctoral dissertation‘Some research on hypothesis testing and nonparametric variable screening problems for high dimensional data’

In this thesis,we construct test statistic for association test and independence test in high dimension,respectively,and study the corresponding theoretical properties under some regularity conditions.Meanwhile,we propose a nonparametric variable screening procedure for sparse additive model with multivariate response in untra-high dimension and established some screening properties.

Variable elliptical vibrating screen: Particles kinematics and industrial application

Traditional vibrating screen usually adopts the linear centralized excitation mode,which causes the difficulty in particles loosening and low screening efficiency.The variable elliptical vibrating screen(VEVS)trajectory is regulated to adapt the material mass along the direction of the screen length,improving the particles distribution as well as the screening efficiency.In this work,a theoretical model was developed for analyzing the screen surface motion law during VEVS-based screening process.An equation was obtained to show the relationship between the horizontal amplitude and the vertical amplitude.The materials kinetic characteristics were studied by using high-speed camera during screening process.Compared with equal-amplitude screen(EAS),the material moving velocity was increased by 13.03%on the first half but decreased by 3.52% on the second half,and the total screening time was reduced by 9.42% by using VEVS.In addition,-6 mm screening test was carried out.At the length of VEVS equaled to 1.2 m,the screening efficiency and the total misplaced material content were 92.50% and 2.90%,respectively.However,the screening efficiency was 89.91% and the total misplaced material content was 3.76% during EAS-based screening process.Furthermore,when external moisture is 5.96%,the screening efficiency of VEVS could reach 86.95%.The 2 TKB50113 type VEVS with double-layered screen surface used in Huoshizui Coal Mine was 5.0 m in width and 11.3 m in length.The areas of single layer and double layer were 56.5 and 113 m~2,respectively.In industrial production,the processing capacity was 2500-3000 t/h and the screening efficiency was larger than 90%.

Feature selection of ultrahigh-dimensional covariates with survival outcomes:a selective review

Many modern biomedical studies have yielded survival data with high-throughput predictors.The goals of scientific research often lie in identifying predictive biomarkers,understanding biological mechanisms and making accurate and precise predictions.Variable screening is a crucial first step in achieving these goals.This work conducts a selective review of feature screening procedures for survival data with ultrahigh dimensional covariates.We present the main methodologies,along with the key conditions that ensure sure screening properties.The practical utility of these methods is examined via extensive simulations.We conclude the review with some future opportunities in this field.

Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates

This paper considers the feature screening and variable selection for ultrahigh dimensional covariates. The new feature screening procedure base on conditional expectation which is used to differentiate whether an explanatory variable contributes to a response variable or not, without requiring a specific parametric form of the underlying data model. The authors estimate the marginal condi- tional expectation by kernel regression estimator. The proposed method is showed to have sure screen property. The authors propose an iterative kernel estimator algorithm to reduce the ultrahigh dimensionality to an appropriate scale. Simulation results and real data analysis demonstrate the proposed method works well and performs better than competing methods.

Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data

This paper considers the iterative sequential lasso(ISLasso)variable selection for generalized linear model with ultrahigh dimensional feature space.The ISLasso selects features by estimated parameter sequentially iteratively for the second order approximation of likelihood function where the features selected depend on regulatory parameters.The procedure stops when extended BIC(EBIC)reaches a minimum.Simulation study demonstrates that the new method is a desirable approach over other methods.

Gini Correlation for Feature Screening

In this paper we propose the Gini correlation screening(GCS)method to select the important variables with ultrahigh dimensional data.The new procedure is based on the Gini correlation coefficient via the covariance between the response and the rank of the predictor variables rather than the Pearson correlation and the Kendallτcorrelation coefficient.The new method does not require imposing a specific model structure on regression functions and only needs the condition which the predictors and response have continuous distribution function.We demonstrate that,with the number of predictors growing at an exponential rate of the sample size,the proposed procedure possesses consistency in ranking,which is both useful in its own right and can lead to consistency in selection.The procedure is computationally efficient and simple,and exhibits a competent empirical performance in our intensive simulations and real data analysis.

A selective overview of sparse sufficient dimension reduction

High-dimensional data analysis has been a challenging issue in statistics.Sufficient dimension reduction aims to reduce the dimension of the predictors by replacing the original predictors with a minimal set of their linear combinations without loss of information.However,the estimated linear combinations generally consist of all of the variables,making it difficult to interpret.To circumvent this difficulty,sparse sufficient dimension reduction methods were proposed to conduct model-free variable selection or screening within the framework of sufficient dimension reduction.Wereview the current literature of sparse sufficient dimension reduction and do some further investigation in this paper.