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A New Approach to Predict Financial Failure: Classification and Regression Trees (CART) 被引量:1
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作者 Ayse Guel Yllgoer UEmit Dogrul Guelhan Orekici Temel 《Journal of Modern Accounting and Auditing》 2011年第4期329-339,共11页
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
关键词 business failure financial distress PREDICTION classification and regression trees (cart
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Groundwater level prediction of landslide based on classification and regression tree 被引量:2
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作者 Yannan Zhao Yuan Li +1 位作者 Lifen Zhang Qiuliang Wang 《Geodesy and Geodynamics》 2016年第5期348-355,共8页
According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the chang... According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree(CART) model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine(SVM) model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides. 展开更多
关键词 LANDSLIDE Groundwater level PREDICTION classification and regression tree Three Gorges Reservoir area
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Machine Learning-Driven Classification for Enhanced Rule Proposal Framework
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作者 B.Gomathi R.Manimegalai +1 位作者 Srivatsan Santhanam Atreya Biswas 《Computer Systems Science & Engineering》 2024年第6期1749-1765,共17页
In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been auto... In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes. 展开更多
关键词 classification and regression tree process automation rules engine model interpretability explainability model trust
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基于CART决策树的分布式数据离群点检测算法
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作者 朱华 乔勇进 董国钢 《现代电子技术》 北大核心 2024年第16期157-162,共6页
在分布式计算环境中,离群点通常表示数据中的异常情况,例如故障、欺诈、攻击等。通过检测分布式数据的离群点,可以对这些异常数据进行集中处理,保护系统和数据的安全。而进行离群点检测时,不仅要考虑数据的规模和复杂性,还要在分布式环... 在分布式计算环境中,离群点通常表示数据中的异常情况,例如故障、欺诈、攻击等。通过检测分布式数据的离群点,可以对这些异常数据进行集中处理,保护系统和数据的安全。而进行离群点检测时,不仅要考虑数据的规模和复杂性,还要在分布式环境下高效地发现离群点。因此,提出一种基于CART决策树的分布式数据离群点检测算法。在构建CART决策树时,使用类间中心距离作为分裂准则,根据分离类别对训练数据进行分类,从而确定数据的类型。在上述基础上,考虑到离群点的分布模式与其周围数据对象不同,使用空间局部偏离因子(SLDF)对空间内各个数据对象之间的离群程度展开度量,同时在高维空间内展开网格划分,引入SLDF算法检测剩余离群点集,最终实现分布式数据离群点检测。实验结果表明,所提方法的离散点检测错误率在0.010以内,可以更加精准地实现分布式数据离群点检测,具有良好的检测性能。 展开更多
关键词 cart决策树 分布式数据 离群点检测 类间距离 数据分类 空间局部偏离因子
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基于CART回归树模型的变电站施工安全事故分析与预测 被引量:1
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作者 田浩 卢博 +3 位作者 杨彦东 卜剑冲 邓建新 李东昌 《湘潭大学学报(自然科学版)》 CAS 2024年第1期101-108,共8页
在当前的变电站施工过程中,主要通过数据包络分析过程预测安全事故,忽略了表征信息中的不确定性,导致预测结果的选取受试者工作特征曲线下面积(AUC)值较低.针对这一问题,本研究应用分类回归树(CART)模型,设计了一种新的变电站施工安全... 在当前的变电站施工过程中,主要通过数据包络分析过程预测安全事故,忽略了表征信息中的不确定性,导致预测结果的选取受试者工作特征曲线下面积(AUC)值较低.针对这一问题,本研究应用分类回归树(CART)模型,设计了一种新的变电站施工安全事故分析与预测方法.首先,利用固定型、移动型采集技术相结合的方式,采集变电站施工现场数据,并通过主成分分析算法进行筛选处理.然后,深入分析变电站施工安全事故发生过程,通过基于概率分布的可分性判据,提取施工安全事故前兆特征.最后,利用CART模型构建施工安全事故根节点,再使用支持向量机(SVM)回归算法建立叶节点,形成可用于施工安全事故预测的最优决策树.通过迭代训练多个串联的CART模型实现梯度提升,应用该模型即可得到准确的事故预测结果.实验结果表明:该预测方法灵敏度更高,能够预测出更多的安全事故,并且该预测方法的AUC值高达0.91,具有更高的预测精度. 展开更多
关键词 分类回归树 变电站施工 安全事故 预测 特征分类 支持向量机
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Predicting the Underlying Structure for Phylogenetic Trees Using Neural Networks and Logistic Regression
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作者 Hassan W. Kayondo Samuel Mwalili 《Open Journal of Statistics》 2020年第2期239-251,共13页
Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ ... Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ from those needed when a population is not structured. In this paper, we compared two supervised machine learning techniques, that is artificial neural network (ANN) and logistic regression models for prediction of an underlying structure for phylogenetic trees. We carried out parameter tuning for the models to identify optimal models. We then performed 10-fold cross-validation on the optimal models for both logistic regression?and ANN. We also performed a non-supervised technique called clustering to identify the number of clusters that could be identified from simulated phylogenetic trees. The trees were from?both structured?and non-structured populations. Clustering and prediction using classification techniques were?done using tree statistics such as Colless, Sackin and cophenetic indices, among others. Results from 10-fold cross-validation revealed that both logistic regression and ANN models had comparable results, with both models having average accuracy rates of over 0.75. Most of the clustering indices used resulted in 2 or 3 as the optimal number of clusters. 展开更多
关键词 Artificial NEURAL Networks LOGISTIC regression PHYLOGENETIC tree tree STATISTICS classification Clustering
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基于CART决策树的110 kV供电区域分布式光伏承载能力测算模型
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作者 代守乐 李萍 《分布式能源》 2024年第3期82-88,共7页
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
关键词 分类与回归树(cart) 110kV供电区域 分布式光伏 承载能力
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Building a Tree Adjusted Logistic Classification Model in Biomarker Data Analyses
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作者 Dion Chen 《Journal of Mathematics and System Science》 2014年第6期433-438,共6页
Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification.... Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification. In this paper, a new method of using a tree regression to improve logistic classification model is introduced in biomarker data analysis. The numerical results show that the linear logistic model can be significantly improved by a tree regression on the residuals. Although the classification problem of binary responses is discussed in this research, the idea is easy to extend to the classification of multinomial responses. 展开更多
关键词 BIOINFORMATICS BIOMARKER tree regression logistic model classification
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基于DRSA和CART的桥梁震损状态决策关联规则提取
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作者 马东辉 罗立 +2 位作者 王威 郭小东 刘朝峰 《北京工业大学学报》 CAS CSCD 北大核心 2023年第11期1167-1179,共13页
为了解决桥梁在地震作用下发生各类损伤与不同影响因素之间的关系复杂且难以发现的问题,将桥梁的破坏情况与桥墩类型、结构类型、支座类型、平面线形、基础类型、交角、断层距、峰值地面加速度等影响因素相结合,利用优势关系粗糙集对桥... 为了解决桥梁在地震作用下发生各类损伤与不同影响因素之间的关系复杂且难以发现的问题,将桥梁的破坏情况与桥墩类型、结构类型、支座类型、平面线形、基础类型、交角、断层距、峰值地面加速度等影响因素相结合,利用优势关系粗糙集对桥梁损伤的条件属性进行约简,提取了汶川地震中374座桥梁发生各类破坏的最少关联规则集,另选20座桥梁进行测试,将结果与不可分辨关系粗糙集理论和分类回归树算法进行对比,结果表明对于震后桥梁损伤状态评价决策关联规则,采用基于优势关系粗糙集理论生成的规则比传统的基于不可分辨关系粗糙集理论和分类回归树算法生成的规则更加准确,因此在今后的桥梁损伤判别研究和规则提取中可以加以利用。 展开更多
关键词 桥梁 震损 优势关系粗糙集 决策树 分类回归树 关联规则
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An Embedded Feature Selection Method for Imbalanced Data Classification 被引量:15
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作者 Haoyue Liu MengChu Zhou Qing Liu 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第3期703-715,共13页
Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority cl... Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index(WGI) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve(ROC AUC) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20% or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem. 展开更多
关键词 classification and regression tree FEATURE selection imbalanced data WEIGHTED GINI INDEX (WGI)
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基于CART-熵权法的管道腐蚀状态评估及其应用 被引量:4
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作者 闻亚星 吕坦 +3 位作者 国滨 王锋 陈金忠 马义来 《腐蚀与防护》 CAS CSCD 北大核心 2023年第9期16-21,100,共7页
管道腐蚀状态评估是管道完整性管理的重要部分,为了评估管道腐蚀状态,根据长输管道腐蚀特点,采用两轮内检测数据计算管道的局部腐蚀速率。以局部腐蚀速率为依据,采用分类与回归树(CART)将管道划分为若干单元,利用熵权法建立腐蚀状态评... 管道腐蚀状态评估是管道完整性管理的重要部分,为了评估管道腐蚀状态,根据长输管道腐蚀特点,采用两轮内检测数据计算管道的局部腐蚀速率。以局部腐蚀速率为依据,采用分类与回归树(CART)将管道划分为若干单元,利用熵权法建立腐蚀状态评估模型,并结合工程实例分析了管道腐蚀状态的相对等级。结果表明:平均预估维修比(ERF)对该管道腐蚀状态的影响最大,该模型确定了腐蚀最严重的管段为44、38、45、37单元,便于业主对这些管段进行重点监测和维修;基于CART-熵权法的腐蚀状态评估模型能够很好地用于管道腐蚀状态评估工作,为业主制定检维修策略提供科学合理的依据。 展开更多
关键词 分类与回归树(cart) 熵权法 管道单元划分 腐蚀状态评估
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基于面向对象CART决策树的土地利用分类研究 被引量:2
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作者 张静懿 王金亮 +2 位作者 胡文英 张硕 王帆 《地理空间信息》 2023年第1期113-118,共6页
面向对象的CART决策树分类方法可解决目前流行的监督分类、非监督分类以及模糊分类方法中“同物异谱、异物同谱”引发的漏分、错分问题。该方法融入了形状和纹理特征进行分类,同时运用二级分类体系解决了相似地物因光谱、纹理不同而导... 面向对象的CART决策树分类方法可解决目前流行的监督分类、非监督分类以及模糊分类方法中“同物异谱、异物同谱”引发的漏分、错分问题。该方法融入了形状和纹理特征进行分类,同时运用二级分类体系解决了相似地物因光谱、纹理不同而导致的地物错分问题,分类效果较好。利用楚雄市鹿城镇2013年GF-1号遥感影像进行土地利用分类。结果表明:(1)基于光谱、形状和纹理信息选取的19个特征变量开展面向对象的CART决策树分类,总体精度可达90.22%,其中林地分类的效果最好;(2)二级分类体系解决了耕地、裸地因光谱、纹理特征多样而产生的地物错分问题,总体精度提高了7.06%,Kappa系数提高了8.17%。 展开更多
关键词 GF-1号 cart决策树分类 楚雄市鹿城镇
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Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features 被引量:1
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作者 N.Kins Burk Sunil R.Ganesan B.Sankaragomathi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2019年第2期351-375,共25页
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
关键词 OBSTRUCTIVE sleep APNEA photoplethysmogram SIGNAL time DOMAIN FEATURES frequency DOMAIN FEATURES classification and regression tree classifIER particle swarm optimization algorithm.
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列线图与CART决策树模型对老年脑卒中患者病耻感预测效能的比较 被引量:4
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作者 张程婕 井坤娟 刘冬雪 《护理学报》 北大核心 2023年第11期7-12,共6页
目的构建列线图与CART决策树模型,探讨2种模型预测老年脑卒中患者发生病耻感风险的应用价值,为早期识别和干预高危患者提供参考。方法基于课题组于2020年5~10月对保定市某三级甲等医院住院的252例老年脑卒中患者病耻感研究资料。将资料... 目的构建列线图与CART决策树模型,探讨2种模型预测老年脑卒中患者发生病耻感风险的应用价值,为早期识别和干预高危患者提供参考。方法基于课题组于2020年5~10月对保定市某三级甲等医院住院的252例老年脑卒中患者病耻感研究资料。将资料按照是否发生病耻感分为无病耻感组(n=125)和有病耻感组(n=127),建立列线图模型和CART决策树模型。内部验证采用Bootstrap重采样1000次的方法,通过AUC、灵敏度、特异度、阳性预测值、阴性预测值、预测准确率对2种模型的预测性能进行比较。结果CART决策树和列线图模型的AUC分别为0.903和0.880,灵敏度分别为90.6%和86.8%,特异度分别为86.0%和80.0%,阳性预测值分别为85.1%和77.8%,阴性预测值分别为90.7%和87.4%,预测准确率分别为88.0%和82.6%。2个模型AUC值相比,差异有统计学意义(t=19.814,P<0.001)。结论2种模型均具有较高的准确性和较好的指导价值,CART决策树预测性能略优于列线图,可为有效评估患者病耻感和采取针对性的干预措施提供指导。 展开更多
关键词 老年脑卒中 病耻感 列线图 决策树 预测模型
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考虑颜色特征最优组合的CART决策树火灾图像识别方法 被引量:1
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作者 李海 孙鹏 《中国安全生产科学技术》 CAS CSCD 北大核心 2023年第1期202-208,共7页
针对火灾图像识别过程中颜色特征数量多、特征间相关性复杂、难以在多维特征融合过程中有效融合图像颜色特征等问题,提出1种考虑颜色特征最优组合的CART决策树火灾图像识别方法。首先,在Lab、RGB、HSV 3种色彩模式下基于图像颜色特征提... 针对火灾图像识别过程中颜色特征数量多、特征间相关性复杂、难以在多维特征融合过程中有效融合图像颜色特征等问题,提出1种考虑颜色特征最优组合的CART决策树火灾图像识别方法。首先,在Lab、RGB、HSV 3种色彩模式下基于图像颜色特征提取火灾图像特征序列;其次,分别在3种色彩模式下基于精细决策树与特征随机排列组合方法提取颜色特征中最优组合特征;最后,将提取的火灾图像最优组合特征序列作为CART决策树输入进行模型训练,并通过测试样本以及其他机器学习方法进行模型泛化能力的分析。研究结果表明:本文方法寻找出识别火灾图像的最优颜色特征组合为“Kb1+Var1+Kg+Kb2+Var2+Kh+Ks+Kv”;CART决策树方法对于火灾图像识别的测试准确度可达84.5%,其分类效果明显优于其他决策树类与集成树类方法;9折为最佳交叉验证折数,其测试准确度可达86.47%,与5折交叉验证相比明显提升14.77%。研究结果可为火灾图像识别提供方法基础。 展开更多
关键词 图像识别 特征贡献度 cart决策树 优化决策树 基尼指数
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A retinal blood vessel extraction algorithm based on CART decision tree and improved AdaBoost
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作者 DIWU Peng-peng HU Ya-qi 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第1期61-68,共8页
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
关键词 classification and regression tree (cart) improved adptive boosting (AdaBoost) retinal blood vessel local binary pattern (LBP) texture
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数据挖掘算法在作业车间调度问题中的应用 被引量:1
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作者 王艳红 赵也践 刘文鑫 《计算机集成制造系统》 EI CSCD 北大核心 2024年第2期520-536,共17页
为了从与日俱增的车间生产数据中提取调度规则来指导生产调度任务,提出一种基于数据挖掘的调度算法。将最小化最大完工时间设置为性能指标,从作业车间的离线生产数据中建立合适的调度样本集;将建立的调度样本集按合适的比例分为训练集... 为了从与日俱增的车间生产数据中提取调度规则来指导生产调度任务,提出一种基于数据挖掘的调度算法。将最小化最大完工时间设置为性能指标,从作业车间的离线生产数据中建立合适的调度样本集;将建立的调度样本集按合适的比例分为训练集和测试集;用数据挖掘算法中的分类回归树(CART)从训练集中获取有效的调度知识,形成CART树状调度规则库;为了验证所得调度规则的有效性,将调度规则与遗传算法结合,设计了一种基于数据挖掘和调度规则的遗传算法作为调度算法来求解作业车间调度问题。通过对不同作业车间经典算例进行仿真与测试,验证了所提调度规则和调度算法的有效性与优越性。 展开更多
关键词 数据挖掘 作业车间调度 分类回归树 调度规则
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一种面向对象的CART决策树火烧迹地提取方法
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作者 牛佳威 《北京测绘》 2023年第5期649-654,共6页
现有的火烧迹地遥感提取主要侧重于对光谱信息的判识,对遥感影像的形状、纹理、空间上下文等特征的挖掘尚不充分。为此,本文提出了一种面向对象的分类回归树算法(CART)决策树火烧迹地提取方法,旨在提升火烧迹地遥感信息提取的精度和可... 现有的火烧迹地遥感提取主要侧重于对光谱信息的判识,对遥感影像的形状、纹理、空间上下文等特征的挖掘尚不充分。为此,本文提出了一种面向对象的分类回归树算法(CART)决策树火烧迹地提取方法,旨在提升火烧迹地遥感信息提取的精度和可靠性。为验证方法的可行性,本文选取四川省冕宁县“4·20”森林火灾为研究区,以国产高分一号B星(GF-1B)卫星数据为数据源,对研究区影像进行面向对象的最优尺度分割,并采用CART决策树算法,根据不同地物的光谱、形状和纹理特征从中自动获取最优特征及其阈值,构建决策树实现火烧迹地提取。结果表明:该方法在火烧迹地上的提取精度(总体精度92.00%)和可靠性(Kappa系数85.56%)均优于既有的监督分类技术方法。相关研究方法和实验结果可为火烧迹地精准提取与灾后评估等研究提供参考。 展开更多
关键词 火烧迹地 高分一号B星(GF-1B) 面向对象分类 最优尺度分割 分类回归树算法(cart)决策树 特征选取
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基于CART集成学习的城市不透水层百分比遥感估算 被引量:21
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作者 廖明生 江利明 +1 位作者 林珲 杨立民 《武汉大学学报(信息科学版)》 EI CSCD 北大核心 2007年第12期1099-1102,1106,共5页
利用Landsat ETM+遥感数据,提出了一种基于CART集成学习的ISP遥感亚像元估算方法,将Boosting重采样技术引入CART分析中,用于提高ISP估算的精度。实验结果表明,该方法的ISP估算性能优于传统的单一CART学习算法,从ETM+影像中估算的ISP值... 利用Landsat ETM+遥感数据,提出了一种基于CART集成学习的ISP遥感亚像元估算方法,将Boosting重采样技术引入CART分析中,用于提高ISP估算的精度。实验结果表明,该方法的ISP估算性能优于传统的单一CART学习算法,从ETM+影像中估算的ISP值与真实值之间的相关系数达到0.91,平均偏差为11.16%。 展开更多
关键词 城市不透水层 遥感影像 分类与回归树 Boosting技术 集成学习
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基于影像多种特征的CART决策树分类方法及其应用 被引量:60
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作者 陈云 戴锦芳 李俊杰 《地理与地理信息科学》 CSCD 北大核心 2008年第2期33-36,共4页
以扬州市宝应县为研究区,采用主成分分析法对研究区影像进行数据压缩和单波段数据增强,利用灰度共生矩阵分析第一主成分的纹理信息。运用基于CART算法的决策树分类方法,选用影像的光谱特征值、NDVI值以及纹理统计量值为测试变量,并通过... 以扬州市宝应县为研究区,采用主成分分析法对研究区影像进行数据压缩和单波段数据增强,利用灰度共生矩阵分析第一主成分的纹理信息。运用基于CART算法的决策树分类方法,选用影像的光谱特征值、NDVI值以及纹理统计量值为测试变量,并通过计算确定决策树的节点规则,提取影像中主要地物信息。将分类结果与单纯依靠光谱特征的监督分类法结果相比较,表明基于影像多种特征的CART决策树分类方法分类精度较高,尤其较好地提取了围网养殖区和建设用地。 展开更多
关键词 纹理特征 光谱特征 cart 决策树
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