The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more ...The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.展开更多
This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) t...This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.展开更多
The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS val...The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.展开更多
Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of ...Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.展开更多
分布式光伏受天气影响较大,测算110kV供电区域的分布式光伏承载能力,对区域供电来说意义重大。基于此,提出基于分类与回归树(calssification and regression tree,CART)的110kV供电区域分布式光伏承载能力测算模型。该模型以分布式电源...分布式光伏受天气影响较大,测算110kV供电区域的分布式光伏承载能力,对区域供电来说意义重大。基于此,提出基于分类与回归树(calssification and regression tree,CART)的110kV供电区域分布式光伏承载能力测算模型。该模型以分布式电源输出功率、区域分布式电源发电量占比、局部分布式电源线损增量等数据为基础,利用CART决策树建立110kV供电区域分布式光伏承载能力测算模型,并使用改进鲸鱼优化算法求解测算结果。经实验测试发现,该模型对分布式光伏承载能力的测算精准度较高,可有效测算不同实验区域在不同季节时的分布式光伏承载能力,具有较高的应用价值。展开更多
以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马...以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马蹄”形结构,黔东、南和西部地区高而中部及北部较低。②基于CART模型的贵州省贫困影响因素重要性的排序为平均隔离度>路网密度>水域比例>平均偏远度>NDVI>年均降水。③根据CART模型决策规则,对贵州省扶贫攻坚提出以下对策建议:首先,应采取更加“精准”的易地扶贫和村镇体系规划降低居民点隔离度,确保居民点之间平均隔离度小于4847 m。其次,在居民点距离确定的基础上,应科学改善区域的生产生活用水条件,将水域面积比例尽可能提升至0.8%以上,保障生活用水和生产灌溉,提升水资源承载能力。最后,在确保居民点隔离度改善,水资源丰度提升的前提下,应重视喀斯特石漠化地区的生态保护修复,将县域的NDVI提升至0.45以上,提高区域生态资产,提升贫困社区韧性,将生态保护与脱贫攻坚相结合,促进区域人地关系和谐发展。展开更多
在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出...在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出力数据的目标函数,利用二阶泰勒展开式对目标函数进行近似处理。结合分位数回归构(Quantile Regression,QR)改进长短期记忆(Long Short Term Memory,LSTM)递归神经网络,构建QRLSTM模型将近似处理后的数据输入至该模型中,通过逻辑门完成新能源出力预测。在测试结果中,实际方法在不同环境条件下对于新能源机组出力情况的预测结果均与实际情况保持较高的拟合度,具有较高的精准度。展开更多
CART(Classification And Regression Tree,分类回归树)是一种准确率和效率都较高的数据挖掘算法,它支持离散型和连续型的数据分类,但无法适用于对加密的隐私云数据进行分类.因此提出PPCART(Privacy-preserving CART,隐私保护的分类回归...CART(Classification And Regression Tree,分类回归树)是一种准确率和效率都较高的数据挖掘算法,它支持离散型和连续型的数据分类,但无法适用于对加密的隐私云数据进行分类.因此提出PPCART(Privacy-preserving CART,隐私保护的分类回归树),该算法利用同态加密特性对CART算法做了相应的改善,使之在保持CART原有准确率和相对较好执行效率的情况下能分类加密云数据,避免了在半诚实模型下的分类过程中真实数据的泄露.经过安全分析和实验测试表明,PPCART可显著提高传统CART算法的安全性,且具有接近于它的执行时间.展开更多
交通拥堵检测是城市交通管理工作的重点和难点之一,现有的拥堵检测以路段为单位,不利于拥堵时空演变规律信息的提取,且检测内容大多只涉及拥堵程度,缺少对拥堵类型的识别。基于CART(classification and regression tree)分类树算法,提...交通拥堵检测是城市交通管理工作的重点和难点之一,现有的拥堵检测以路段为单位,不利于拥堵时空演变规律信息的提取,且检测内容大多只涉及拥堵程度,缺少对拥堵类型的识别。基于CART(classification and regression tree)分类树算法,提出一种以路段点为检测单元的拥堵点分类检测方法,该方法可根据路段平均行驶速度实时检测拥堵点及其类型。首先,将路段等距离划分后映射为路段点,根据时空维路况异常规则和异常模式,以路段点为单元分析了4种拥堵类型的时空演变模式;其次,在路段路况检测的基础上,提取路段点路况时空序列,根据不同类型的拥堵模式对路况时空序列进行分类标记;然后,选取4种速度指标作为样本属性集合,按照属性集合提取各路段点在各时段的速度,以此作为决策树学习的数据集;最后,基于CART分类树算法,采用交叉验证的方式训练出最优模型,使其达到最佳的泛化能力。与支持向量机(support vector machine,SVM)分类模型进行比较,实验结果表明,该方法在分类检测交通拥堵点时具有较高的正确率和召回率,且分类检测时效性较好。展开更多
文摘The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.
基金National Natural Science Foundation of China(No.61163010)
文摘This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.
基金Under the auspices of National Natural Science Foundation of China(No.41671339)
文摘The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.
文摘Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.
文摘分布式光伏受天气影响较大,测算110kV供电区域的分布式光伏承载能力,对区域供电来说意义重大。基于此,提出基于分类与回归树(calssification and regression tree,CART)的110kV供电区域分布式光伏承载能力测算模型。该模型以分布式电源输出功率、区域分布式电源发电量占比、局部分布式电源线损增量等数据为基础,利用CART决策树建立110kV供电区域分布式光伏承载能力测算模型,并使用改进鲸鱼优化算法求解测算结果。经实验测试发现,该模型对分布式光伏承载能力的测算精准度较高,可有效测算不同实验区域在不同季节时的分布式光伏承载能力,具有较高的应用价值。
文摘以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马蹄”形结构,黔东、南和西部地区高而中部及北部较低。②基于CART模型的贵州省贫困影响因素重要性的排序为平均隔离度>路网密度>水域比例>平均偏远度>NDVI>年均降水。③根据CART模型决策规则,对贵州省扶贫攻坚提出以下对策建议:首先,应采取更加“精准”的易地扶贫和村镇体系规划降低居民点隔离度,确保居民点之间平均隔离度小于4847 m。其次,在居民点距离确定的基础上,应科学改善区域的生产生活用水条件,将水域面积比例尽可能提升至0.8%以上,保障生活用水和生产灌溉,提升水资源承载能力。最后,在确保居民点隔离度改善,水资源丰度提升的前提下,应重视喀斯特石漠化地区的生态保护修复,将县域的NDVI提升至0.45以上,提高区域生态资产,提升贫困社区韧性,将生态保护与脱贫攻坚相结合,促进区域人地关系和谐发展。
文摘在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出力数据的目标函数,利用二阶泰勒展开式对目标函数进行近似处理。结合分位数回归构(Quantile Regression,QR)改进长短期记忆(Long Short Term Memory,LSTM)递归神经网络,构建QRLSTM模型将近似处理后的数据输入至该模型中,通过逻辑门完成新能源出力预测。在测试结果中,实际方法在不同环境条件下对于新能源机组出力情况的预测结果均与实际情况保持较高的拟合度,具有较高的精准度。
文摘CART(Classification And Regression Tree,分类回归树)是一种准确率和效率都较高的数据挖掘算法,它支持离散型和连续型的数据分类,但无法适用于对加密的隐私云数据进行分类.因此提出PPCART(Privacy-preserving CART,隐私保护的分类回归树),该算法利用同态加密特性对CART算法做了相应的改善,使之在保持CART原有准确率和相对较好执行效率的情况下能分类加密云数据,避免了在半诚实模型下的分类过程中真实数据的泄露.经过安全分析和实验测试表明,PPCART可显著提高传统CART算法的安全性,且具有接近于它的执行时间.
