Classification of aviation incident causes using LGBM with improved cross-validation

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

基于Cross-Validation的小波自适应去噪方法

小波去噪算法中,阈值的选择非常关键.提出一种自适应阈值选择算法.该算法先通过Cross-Validation方法将噪声干扰信号分成两个子信号,一个用于阈值处理,一个用作参考信号;再采用最深梯度法来寻求一个最优去噪阈值.仿真和实验结果表明:在均方误差意义上,所提算法去噪效果优于Donoho等提出的VisuShrink和SureShrink两种去噪算法,且不需要带噪信号的任何'先验信息',适应于实际信号去噪处理.

Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited

In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation study with 15 predictors and a complex correlation structure in the linear regression model. Using sample sizes of 100 and 400 and estimates of the residual variance corresponding to R2 of 0.50 and 0.71, we consider 4 scenarios with varying amount of information. We also consider two examples with 24 and 13 predictors, respectively. We will discuss the value of cross-validation, shrinkage and backward elimination (BE) with varying significance level. We will assess whether 2-step approaches using global or parameterwise shrinkage (PWSF) can improve selected models and will compare results to models derived with the LASSO procedure. Beside of MSE we will use model sparsity and further criteria for model assessment. The amount of information in the data has an influence on the selected models and the comparison of the procedures. None of the approaches was best in all scenarios. The performance of backward elimination with a suitably chosen significance level was not worse compared to the LASSO and BE models selected were much sparser, an important advantage for interpretation and transportability. Compared to global shrinkage, PWSF had better performance. Provided that the amount of information is not too small, we conclude that BE followed by PWSF is a suitable approach when variable selection is a key part of data analysis.

ON THE CONSISTENCY OF CROSS-VALIDATIONIN NONLINEAR WAVELET REGRESSION ESTIMATION

For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.

Using Multiple Risk Factors and Generalized Linear Mixed Models with 5-Fold Cross-Validation Strategy for Optimal Carotid Plaque Progression Prediction

Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Generalized linear mixed models(GLMM)is an extension of linear model for categorical responses while considering the correlation among observations.Methods Magnetic resonance image(MRI)data of carotid atheroscleroticplaques were acquired from 20 patients with consent obtained and 3D thin-layer models were constructed to calculate plaque stress and strain for plaque progression prediction.Data for ten morphological and biomechanical risk factors included wall thickness(WT),lipid percent(LP),minimum cap thickness(MinCT),plaque area(PA),plaque burden(PB),lumen area(LA),maximum plaque wall stress(MPWS),maximum plaque wall strain(MPWSn),average plaque wall stress(APWS),and average plaque wall strain(APWSn)were extracted from all slices for analysis.Wall thickness increase(WTI),plaque burden increase(PBI)and plaque area increase(PAI) were chosen as three measures for plaque progression.Generalized linear mixed models(GLMM)with 5-fold cross-validation strategy were used to calculate prediction accuracy for each predictor and identify optimal predictor with the highest prediction accuracy defined as sum of sensitivity and specificity.All 201 MRI slices were randomly divided into 4 training subgroups and 1 verification subgroup.The training subgroups were used for model fitting,and the verification subgroup was used to estimate the model.All combinations(total1023)of 10 risk factors were feed to GLMM and the prediction accuracy of each predictor were selected from the point on the ROC(receiver operating characteristic)curve with the highest sum of specificity and sensitivity.Results LA was the best single predictor for PBI with the highest prediction accuracy(1.360 1),and the area under of the ROC curve(AUC)is0.654 0,followed by APWSn(1.336 3)with AUC=0.6342.The optimal predictor among all possible combinations for PBI was the combination of LA,PA,LP,WT,MPWS and MPWSn with prediction accuracy=1.414 6(AUC=0.715 8).LA was once again the best single predictor for PAI with the highest prediction accuracy(1.184 6)with AUC=0.606 4,followed by MPWSn(1. 183 2)with AUC=0.6084.The combination of PA,PB,WT,MPWS,MPWSn and APWSn gave the best prediction accuracy(1.302 5)for PAI,and the AUC value is 0.6657.PA was the best single predictor for WTI with highest prediction accuracy(1.288 7)with AUC=0.641 5,followed by WT(1.254 0),with AUC=0.6097.The combination of PA,PB,WT,LP,MinCT,MPWS and MPWS was the best predictor for WTI with prediction accuracy as 1.314 0,with AUC=0.6552.This indicated that PBI was a more predictable measure than WTI and PAI. The combinational predictors improved prediction accuracy by 9.95%,4.01%and 1.96%over the best single predictors for PAI,PBI and WTI(AUC values improved by9.78%,9.45%,and 2.14%),respectively.Conclusions The use of GLMM with 5-fold cross-validation strategy combining both morphological and biomechanical risk factors could potentially improve the accuracy of carotid plaque progression prediction.This study suggests that a linear combination of multiple predictors can provide potential improvement to existing plaque assessment schemes.

Multi-environment BSA-seq using large F3 populations is able to achieve reliable QTL mapping with high power and resolution: An experimental demonstration in rice

Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.

Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method

In recent years,Kriging model has gained wide popularity in various fields such as space geology,econometrics,and computer experiments.As a result,research on this model has proliferated.In this paper,the authors propose a model averaging estimation based on the best linear unbiased prediction of Kriging model and the leave-one-out cross-validation method,with consideration for the model uncertainty.The authors present a weight selection criterion for the model averaging estimation and provide two theoretical justifications for the proposed method.First,the estimated weight based on the proposed criterion is asymptotically optimal in achieving the lowest possible prediction risk.Second,the proposed method asymptotically assigns all weights to the correctly specified models when the candidate model set includes these models.The effectiveness of the proposed method is verified through numerical analyses.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

Height-diameter models for King Boris fir(Abies borisii regis Mattf.) and Scots pine(Pinus sylvestris L.) in Olympus and Pieria Mountains, Greece

In forest science and practice, the total tree height is one of the basic morphometric attributes at the tree level and it has been closely linked with important stand attributes. In the current research, sixteen nonlinear functions for height prediction were tested in terms of their fitting ability against samples of Abies borisii regis and Pinus sylvestris trees from mountainous forests in central Greece. The fitting procedure was based on generalized nonlinear weighted regression. At the final stage, a five-quantile nonlinear height-diameter model was developed for both species through a quantile regression approach, to estimate the entire conditional distribution of tree height, enabling the evaluation of the diameter impact at various quantiles and providing a comprehensive understanding of the proposed relationship across the distribution. The results clearly showed that employing the diameter as the sole independent variable, the 3-parameter Hossfeld function and the 2-parameter N?slund function managed to explain approximately 84.0% and 81.7% of the total height variance in the case of King Boris fir and Scots pine species, respectively. Furthermore, the models exhibited low levels of error in both cases(2.310m for the fir and 3.004m for the pine), yielding unbiased predictions for both fir(-0.002m) and pine(-0.004m). Notably, all the required assumptions for homogeneity and normality of the associated residuals were achieved through the weighting procedure, while the quantile regression approach provided additional insights into the height-diameter allometry of the specific species. The proposed models can turn into valuable tools for operational forest management planning, particularly for wood production and conservation of mountainous forest ecosystems.

Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms

Parkinson’s disease(PD)is a chronic neurological condition that progresses over time.People start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die.The patient’s symptoms become more severe due to the worsening of their signs over time.In this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors.The research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of PD.We aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD accurately.This research methodology involves developing a model using a machine learning algorithm.In the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning approach.With 98%accuracy,LightGBM had the highest accuracy in this study.RF,KNN,and SVM came in second with 96%accuracy.Furthermore,the performance scores of NB and LR were recorded to be 76%and 83%,respectively.It is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory level.Finally,we extracted meaningful insights and figured out potential gaps on top of PD.By extracting meaningful insights and identifying potential gaps,our study contributes to the significance and impact of PD research.The application of advanced machine learning algorithms holds promise in accurately diagnosing PD and shedding light on crucial aspects of the disease.This research has the potential to enhance the understanding and management of PD,ultimately improving the lives of individuals affected by this condition.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental environment.There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.AIM To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and MDD.All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest scale.We performed a two-sample t-test between the two groups-SCZ patients and depressive patients.Our purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,DN.RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task.We identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)e.g.It is shared between DEP and SCZ,and includes frontal motor/language and parietal areas.One specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus,several occipital areas,including lingual and fusiform gyrus,as well as parahippocampal gyrus.Finally,component 12 appeared to be unique for the neutral condition.In addition,there have been determined circuits across components,which are either common,or distinct in the preferential processing of the sub-scales of the task.CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

OPT-BAG Model for Predicting Student Employability

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job.Based on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the future.In addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their employability.An effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of students.This model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be improved.First,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability dataset.Next,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability dataset.The training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation technique.Finally,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student employability.The empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student employability.In this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most important.This indicates that educational institutions should focus on these factors during the learning process to improve student employability.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

An Adaptive Approach for Hazard Regression Modeling

Regression models for survival time data involve estimation of the hazard rate as a function of predictor variables and associated slope parameters. An adaptive approach is formulated for such hazard regression modeling. The hazard rate is modeled using fractional polynomials, that is, linear combinations of products of power transforms of time together with other available predictors. These fractional polynomial models are restricted to generating positive-valued hazard rates and decreasing survival times. Exponentially distributed survival times are a special case. Parameters are estimated using maximum likelihood estimation allowing for right censored survival times. Models are evaluated and compared using likelihood cross-validation (LCV) scores. LCV scores and tolerance parameters are used to control an adaptive search through alternative fractional polynomial hazard rate models to identify effective models for the underlying survival time data. These methods are demonstrated using two different survival time data sets including survival times for lung cancer patients and for multiple myeloma patients. For the lung cancer data, the hazard rate depends distinctly on time. However, controlling for cell type provides a distinct improvement while the hazard rate depends only on cell type and no longer on time. Furthermore, Cox regression is unable to identify a cell type effect. For the multiple myeloma data, the hazard rate also depends distinctly on time. Moreover, consideration of hemoglobin at diagnosis provides a distinct improvement, the hazard rate still depends distinctly on time, and hemoglobin distinctly moderates the effect of time on the hazard rate. These results indicate that adaptive hazard rate modeling can provide unique insights into survival time data.

直方图理论与最优直方图制作

直方图是一种最为常见的密度估计和数据分析工具.在直方图理论和制作过程中,组距的选择和边界点的确定尤为重要.然而,许多学者对这两个参数的选择仍然采用经验的方法,甚至现在大多数统计软件在确定直方图分组数时也是默认采用粗略的计算公式.本文主要介绍直方图理论和最优直方图制作的最新研究成果,强调面向样本的最优直方图制作方法.

基于V-foldCross-validation和Elman神经网络的信用评价研究

研究了关于公司信用评估问题的现状,指出一般神经网络应用于信用评估领域的不足.在此基础上,提出一套甄选原则以选择关键的信用评分指标;然后依据这些指标建立了基于Elman回归神经网络的我国企业的信用评估模型.采用V-foldCross-validation技巧对该模型的评分效果进行了实证研究.

基于ANFIS和Elman网络的信用评价研究

BP神经网络用作信用等级分类可取得较好的效果,但在过分要求输出信用分值时效果不佳。针对该缺陷,本文采用自适应神经网络(ANFIS)和Elman网络研究公司信用评分。文中提出了一套甄选方法准则,用于建立适合我国企业的信用评分指标体系;然后依据该指标体系建立了基于Elman网络和ANFIS的信用评估模型;采用V foldCross validation技巧,利用样本公司实际指标数据对该模型的评分效果进行了实证研究。

不同模型在信用评价中的比较研究

比较了不同模型应用于企业信用评价问题的优劣,针对信用评分问题特点,采用Elman回归神经网络和BP网络建模。在建立了适合于我国企业的信用评分指标体系之后,运用以上两种方法进行实证研究并比较两种网络的诊断行为;为克服小样本建模的缺点,引进V foldCross validation计算技巧。