基于Boosting学习算法的雷达弹道识别

弹道外推技术在炮位雷达的侦察和校射中起关键作用，弹道外推的精度直接决定着炮位侦察校射雷达的性能。在文献[1]中，作者提出了将弹道外推分为弹道识别和特定弹道外推两个阶段，并用支持向量机方法对弹道识别进行了系统研究。文中引进Boosting学习算法进行弹道识别。仿真结果表明，基于决策树的Boosting学习算法是一种有效的弹道识别方法，并且识别精度高于基于核技巧的支持向量机方法。

基于形状无关纹理和Boosting学习的人口统计学分类

基于形状无关纹理和boosting学习,该文提出了对性别和年龄分类的方法,其中年龄被划分为儿童、青年、中年和老年4类。检测到人脸后,利用人脸配准的结果规范化人脸图像获得形状无关纹理。在此基础上提取Haar型特征、LBP直方图和Gabor Jet3种特征,通过boosting学习分别训练分类器。实验表明,LBP直方图特征能够鲁棒地区分儿童和老人,Haar型特征用作区分青年和中年人则更为有效,而Gabor Jet特征更适于性别分类。

基于Boosting学习的图片自动语义标注

图片自动语义标注是基于内容图像检索中很重要且很有挑战性的工作。本文提出了一种基于Boosting学习的图片自动语义标注方法,建立了一个图片语义标注系统BLIR(boosting for lingu istic indexing im age retrievalsystem)。假设一组具有同一语义的图像能够用一个由一组特征组合而成的视觉模型来表示。2D-MHMM(2维多分辨率隐马尔科夫模型)实际上就是一种颜色和纹理特殊组合的模板。BLIR系统首先生成大量的2D-MHMM模型,然后用Boosting算法来实现关键词与2D-MHMM模型的关联。在一个包含60 000张图像的图库上实现并测试了这个系统。结果表明,对这些测试图像,BLIR方法比其他方法具有更高的检索正确率。

基于迁移学习Boosting的跨时间脑力负荷识别研究

在航天任务中对脑力负荷的有效检测可以保障任务执行效率和生产安全,预测航天员的表现。针对跨时间脑力负荷检测,依据MATB任务设计了包含不同难度任务的跨时间实验范式,采集了14名志愿者3次不同时间的脑电数据。基于迁移学习Boosting方法,引入辅助数据,设计了基于TrAdaboost的跨时间脑力负荷识别算法,在没有目标数据参与的情况下进行了跨时间的分类识别。探索了最佳分段长度和辅助样本比例对识别效果的影响,并基于多个数据样本进行了决策研究,跨时间下的脑力负荷最佳识别准确率达到74.73%。结果表明,提出的跨时间脑力负荷分类框架实现了脑力负荷的有效识别。

基于Boosting集成学习的风险URL检测研究

随着互联网的不断发展,网站数量不断增长,URL作为访问网站的唯一入口,成为Web攻击的重点对象。传统的URL检测方式主要是针对恶意URL,主要方法是基于特征值和黑白名单,容易产生漏报,且对于复杂URL的检测能力不足。为解决上述问题,基于集成学习中的Boosting思想,提出一种针对业务访问的风险URL检测的混合模型。该模型前期将URL作为字符串,使用自然语言处理技术对其进行分词及向量化,然后采用分步建模法的思想,首先利用GBDT算法构建二分类模型,判断URL是否存在风险,接着将风险URL原始字符串输入到多分类模型中,利用XGBoost算法对其进行多分类判定,明确风险URL的具体风险类型,为安全分析人员提供参考。在模型构建过程中不断进行参数调优,并采用AUC值和F1值分别对二分类模型和多分类模型进行评估,评估结果显示二分类模型的AUC值为98.91%,多分类模型的F1值为0.993,效果较好。将其应用到实际环境中,与现有检测手段进行对比,发现模型的检出率高于现有WAF和APT安全设备,其检测结果弥补了现有检测手段的漏报。

基于KPCA⁃XGBoost机器学习的大跨体育场风荷载预测

大跨空间结构风荷载的取值是该类结构抗风设计关注重点,通常借助风洞试验或数值风洞确定,但其费用高周期长等特点限制其广泛应用.机器学习方法近年受到关注,逐渐应用于结构的风荷载预测并取得了不错的效果.利用核主成分分析(Kernel Principal Component Analysis,KPCA)对数据进行降维处理,借助可以集成学习的XGBoost机器学习模型,采用十折交叉验证对超参数进行选择,编写了基于机器学习的大跨空间结构风荷载预测程序.通过对多个已有工程项目风洞试验结果的学习训练和预测结果比对,证明该方法具有处理数据能力较强、预测效率较高及泛化能力较强等特点.随机选取未参与模型训练的风向角下数据进行模型准确性验证,结果表明模型的R2值均达到0.9左右,预测值与试验值较为接近,体型系数在迎风区的预测精度略低于背风区,而极值风压则在背风区的预测精度好于迎风区.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

应用于工控数据异常检测的改进TrAdaBoost迁移学习算法研究

为解决工业控制系统多场景异常检测模型迁移难题,提出了一种基于特征排序和遗传算法的TrAdaBoost迁移学习的工控异常检测算法。由于TrAdaBoost要求同维数,首先采用特征选择算法Lasso做特征排序,然后根据排序结果选择重要特征。其次,将源域与目标域数据分布情况与回补参数相结合更新样本权重,减少权重两极化情况。最后采用遗传算法对TrAdaBoost在目标域上基分类器数目进行参数优化,以提高算法在数据上的适应性。将算法在新加坡数据集和自建数据集上进行实验,使用源域数据集进行迁移实验后,正确率提高9.1%,该算法对知识迁移和样本不足的问题提出了更好的解决方案。

多源域分布下优化权重的迁移学习Boosting方法

深度决策树迁移学习Boosting方法(DTrBoost)仅能适应一个源域与一个目标域的训练数据,无法适应多个不同分布的源域的样本。此外,DTrBoost方法同步地从源域中学习数据至目标域模型,并没有根据重要程度量化学习知识的权重。在实践中,对于某数据集的数据按照某一或某些特征划分出来的数据往往分布不一致,并且这些不同分布的数据对于最终模型的重要性也不一致,知识迁移的权重也因此不平等。针对这一问题,提出了多源域优化权重的迁移学习方法,主要思想是根据不同分布的源域空间计算出到目标域的KL距离,利用KL距离的比值计算出不同分布的源域样本的学习权重比例,从而优化整体梯度函数,使学习方向朝着梯度下降最快的方向进行。使用梯度下降算法能使模型较快收敛,在确保迁移学习效果的同时,也能确保学习的速度。实验结果表明,提出的算法在整体上实现了更好的性能并且对于不同的训练数据能够实现自适应效果,分类错误率平均下降0.013,在效果最好的OCR数据集上下降0.030。

Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

基于改进Boosting算法的车险理赔额组合模型预测

针对车险理赔额预测中单一机器学习方法存在的问题,提出一种基于Optuna调参后的XGBoost(eXtreme Gradient Boosting)-LightGBM(Light Gradient Boosting Machine)组合模型预测方法。首先,分别构建XGBoost与LightGBM单个模型,并使用Optuna框架对模型参数进行优化;其次,将2个优化后的模型预测结果进行加权融合;最后,采用法国第三方责任险的车险保单数对融合模型进行验证。结果表明,与单一的XGBoost和LightGBM模型相比,经过参数优化后的组合模型在预测车险理赔额时展现出更低的均方根误差,从而证明其更高的预测精度。

基于XGBoost机器学习算法的肺结节浸润性预测模型构建与验证:一项双中心研究

目的采用XGBoost机器学习算法构建一个临床影像模型,预测肺结节病理浸润性,并在一个外部验证组中对模型进行泛化性验证。方法回顾性纳入CT诊断为孤立性肺结节患者248例,分别提取肺结节区域和结节周围3mm、5mm区域的放射组学特征。经过从粗到细的特征选择后,使用最小绝对收缩和选择算子(LASSO)方法计算Radscore。采用单因素和多因素Logistic回归分析筛选与肺结节浸润性相关的临床放射学因素。然后,利用Logistic和XGBoost算法构建临床-放射组学联合模型,在一个独立的外部验证组(n=147)中评估模型的泛化性能。结果综合Radscore、CT值、肺结节长度、月牙征的临床放射学XGBoost联合模型对肺结节浸润性的预测效果优于放射组学模型、临床放射学Logistic联合模型,在训练队列中的曲线下面积AUC为0.889(95%CI,0.848~0.927),在外部验证组中曲线下面积AUC为0.889(95%CI,0.823~0.942)。结论我们采用XGBoost机器学习算法构建了一种预测肺结节浸润性的临床放射学模型,结果显示出令人满意的预测效能,并在一个独立外部验证组中得到了良好的泛化性验证,可以帮助临床医生指导肺结节的诊疗并制定评估策略。

Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.

基于K-Means聚类和Boosting算法的配电网线损计算方法

传统线损计算方法所需电气参数较多且计算过程烦琐,导致配电网线损计算结果精度较低,因此提出了一种基于K-Means聚类和Boosting算法的配电网线损计算方法。先采用K-Means聚类算法挖掘配电网的线路负荷有功电量、线路负荷无功电量、线路长度及线路负载率等电气特征指标,再将电气特征指标作为Boosting算法线损预测模型的输入数据,经过模型训练完成配电网线损的预测计算。实验结果表明,该设计方法的线损计算值与真实值之间的误差仅为4.27%,具有较高的配电网线损计算精度。

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports

BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.

基于AdaBoost学习策略的污水近红外光谱快速检测

对实际污水样本的近红外光谱数据进行建模,可以预测水质指标,实现污水水质监测。但实际污水样本的多样性不足,标签值集中在某个较低的区间内,样本间离散度低、区分度小,导致近红外光谱数据和标签值间的相关性较弱,一般的分类模型和回归模型的预测准确度较低。因此,利用自适应增强(adaptive boosting,AdaBoost)算法进行建模以提高模型的准确度,利用集成策略将多个子学习器组合为一个准确度更高的强学习器。此外,人为配置具有浓度梯度的标准样本对实际污水样本进行补充,以减弱实际污水样本的多样性不足对建模精度的影响。在不同数据集上对AdaBoost算法和其他常用算法进行了对比,对比结果证明了AdaBoost算法在污水水质快速检测方面的有效性。

基于极限学习机和boosting多核学习的目标跟踪算法

如何构造鲁棒的分类器一直是基于判别式的目标跟踪算法研究的热点,近些年多核学习通过线性组合多个核分类器达到了更好的分类性能,受到了广泛的关注。传统的多核学习需要解复杂的最优化问题,很难直接应用到目标跟踪中,因此提出一种基于boosting学习框架的多核学习算法,使目标跟踪在复杂场景下可以保持跟踪的实时性和准确性。为了进一步减少计算量和提升分类性能,采用极限学习机(extreme learning machine,ELM)作为基分类器,ELM结构简单,训练速度非常快,并且比支持向量机有更好的泛化能力。最后,将本文算法与其他先进的跟踪算法在多个公开视频序列中进行比较,验证了本文算法性能的有效性。

基于XGBoost算法的在线学习多时段发展性评价模型研究

在线学习的评价研究是促进智慧教育评价发展的重要部分。现有的在线学习评价研究主要集中在利用学习行为理论进行学习效果评价,而对学生在学习过程中的能力发展评价的研究较少。为此,本研究通过结合发展性评价理论、已有评价体系及线上学习反馈数据,构建自适应的发展性评价分析框架,确定了以学习态度、学习效果、学习策略、学习交互为一级指标的在线学习发展性评价体系;通过构建基于XGBoost算法的多时段学习过程回归模型,重构评价指标。最终,应用评价模型开展学生行为分析和教师看板分析。本研究一定程度拓展了智慧教育的评价维度,为数字化教育的过程分析提供数据决策。

多源域分布下优化权重的无监督迁移学习Boosting方法

深度决策树迁移学习Boosting方法(DTrBoost)可以有效地实现单源域有监督情况下向一个目标域迁移学习,但无法实现多个源域情况下的无监督迁移场景。针对这一问题,提出了多源域分布下优化权重的无监督迁移学习Boosting方法,主要思想是根据不同源域与目标域分布情况计算出对应的KL值,通过比较选择合适数量的不同源域样本训练分类器并对目标域样本打上伪标签。最后,依照各个不同源域的KL距离分配不同的学习权重,将带标签的各个源域样本与带伪标签的目标域进行集成训练得到最终结果。对比实验表明,提出的算法实现了更好的分类精度并对不同的数据集实现了自适应效果,分类错误率平均下降2.4%,在效果最好的marketing数据集上下降6%以上。

基于CatBoost集成学习的边坡稳定性预测方法

为对边坡稳定性进行广义高效地准确判断,引入了一种新型的CatBoost集成学习算法评价边坡稳定性。根据边坡基本几何与地质要素,无需数值建模与计算,直接客观地评判边坡稳定性状态,并从概率信息论角度给出边坡稳定性概率,实现速度快、精度高、稳健性好的广域尺度下边坡稳定性评价。以影响边坡稳定性的5个主要因素作为评价指标,创造了大型边坡稳定性评价数据集,据此构建了基于CatBoost集成学习的边坡稳定性预测模型。仿真结果表明,对比常见机器学习和代表性集成学习模型,CatBoost模型预测效果明显优于其他模型,且减少了超参数调优需求,更具有通用性、客观性和可靠性,可有效应用于边坡稳定性初步评价。通过举例仁化(湘粤界)至博罗高速公路仁化至新丰段某边坡工程,验证了基于CatBoost集成学习的边坡稳定性评价方法的可行性。