检索结果-内蒙古大学图书馆

Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

引用

ENVIRONMENTAL EARTH SCIENCES 2018年第8期77卷 1-13页

作者： Choubin, Bahram Zehtabian, Gholamreza Azareh, Ali Rafiei-Sardooi, Elham Sajedi-Hosseini, Farzaneh Kisi, Ozgur Sari Agr Sci & Nat Resources Univ Dept Watershed Management POB 737 Sari Iran Univ Tehran Dept Reclamat Arid & Mountainous Reg Karaj 315853314 Iran Univ Jiroft Dept Geog Kerman Iran Univ Jiroft Fac Nat Resources Kerman Iran Ilia State Univ Fac Nat Sci & Engn Tbilisi Georgia

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash-Sutcliffe efficiency, NSE > 0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked "very good" for the CART method, while the ANFIS and the time series model approximately indicated "satisfactory" and "unsatisfactory" performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.

关键词： Large-scale climate predictors classification and regression trees Machine learning Semiarid region

来源：评论

学校读者我要写书评

暂无评论

IPO mechanism selection by using classification and regression trees

引用

QUALITY & QUANTITY 2012年第3期46卷 873-888页

作者： Kucukkocaoglu, Guray Alp, Ozge Sezgin Baskent Univ Dept Management Fac Econ & Adm Sci TR-06490 Ankara Turkey Baskent Univ Fac Commercial Sci Dept Accounting & Financial Management TR-06490 Ankara Turkey

The Turkish IPO market gives issuers and underwriters a choice of three different IPO selling mechanisms. The current paper sheds new light on the determinants of these issue procedures within the context of the following methods (i) book building mechanism, (ii) fixed price offer, and (iii) sale through the stock exchange. Most of the empirical models in the IPO literature use binary probit and logit models to determine the factors behind the choice of one method over another and try to answer the question of "why is such a mechanism chosen". To understand the reasons on issuers' selection of IPO mechanism, we have conducted a classification and regression trees (CART) methodology to represent decision rules in a form of binary trees. Our results indicate that, CART methodology predicts a firms' IPO selling mechanism with 77.42% accuracy. The most important variable that determines the IPO selling mechanism is the Arrangement Type between the issuer and the underwriter as in the form of best effort and firm-commitment.

关键词： IPO selling mechanisms classification and regression trees

来源：评论

学校读者我要写书评

暂无评论

Lost in the middle - a pragmatic approach for ERP managers to prioritize known vulnerabilities by applying classification and regression trees (CART)

引用

INFORMATION AND COMPUTER SECURITY 2023年第5期31卷 655-674页

作者： Mathieu, Richard G. Turovlin, Alan E. Johnson & Wales Univ Charlotte NC USA Univ North Carolina Business Informat Syst & Operat Management Dept Charlotte NC 28223 USA

PurposeCyber risk has significantly increased over the past twenty years. In many organizations, data and operations are managed through a complex technology stack underpinned by an Enterprise Resource Planning (ERP) system such as systemanalyse programmentwicklung (SAP). The ERP environment by itself can be overwhelming for a typical ERP Manager, coupled with increasing cybersecurity issues that arise creating periods of intense time pressure, stress and workload, increasing risk to the organization. This paper aims to identify a pragmatic approach to prioritize vulnerabilities for the ERP ***/methodology/approachApplying attention-based theory, a pragmatic approach is developed to prioritize an organization's response to the National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) vulnerabilities using a classification and regression Tree (CART).FindingsThe application of classification and regression tree (CART) to the National Institute of Standards and Technology's National Vulnerability Database identifies prioritization unavailable within the NIST's *** implicationsThe ERP Manager is a role between technology, functionality, centralized control and organization data. Without CART, vulnerabilities are left to a reactive approach, subject to overwhelming situations due to intense time pressure, stress and ***/valueTo the best of the authors' knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. CART has previously not been applied to the prioritizing cybersecurity vulnerabilities.

关键词： Enterprise systems ERP Vulnerability analysis CART classification and regression trees National vulnerability database

来源：评论

学校读者我要写书评

暂无评论

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and classification and regression trees

引用

GEOMORPHOLOGY 2018年 303卷 256-270页

作者： Binh Thai Pham Prakash, Indra Dieu Tien Bui Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Govt Gujarati Bhaskarcharya Inst Space Applicat & Geoinformat B Dept Sci & Technol Gandhinagar India

A hybrid machine learning approach of Random Subspace (RSS) and classification and regression trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Nave Bayes trees (NBT), and Logistic regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Landslide susceptibility map Machine learning Emsembles Random Subspace classification and regression trees

来源：评论

学校读者我要写书评

暂无评论

Mathematical optimization in classification and regression trees

引用

TOP 2021年第1期29卷 5-33页

作者： Carrizosa, Emilio Molero-Rio, Cristina Romero Morales, Dolores Univ Seville Inst Matemat Seville Spain Copenhagen Business Sch Dept Econ Frederiksberg Denmark

classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data.

关键词： classification and regression trees Tree ensembles Mixed-integer linear optimization Continuous nonlinear optimization Sparsity Explainability

来源：评论

学校读者我要写书评

暂无评论

Integrative exposure assessment through classification and regression trees on bioaccumulation of metals, related sampling site characteristics and ecoregions

引用

ECOLOGICAL INFORMATICS 2006年第1期1卷 55-65页

作者： Pesch, Roland Schroeder, Winfried Hsch Vechta D-49364 Vetcha Germany

The UNECE Heavy Metals in Mosses Surveys measure and spatially predict environmental concentration (PEC) of metals in mosses for ecotoxicological risk assessments. Up to now, no statistical sound investigation was dedicated to those boundary conditions which, aside from the atmospheric depositions of metals from the atmosphere down to the land surface, might influence the bioaccumulation of metals. Thus, the article focuses on the integrative analysis of the data on the bioaccumulation of metals in Germany 1990, 1995 and 2000 on the one hand and of data on conceivable boundary conditions on the other hand. To this end classification and regression trees (CART) were used because CART is a very powerful statistical technique that enables to uncover structures in large data sets containing categorical and continuous data without any transformation of scale dignity. Within the framework of the Metals in Mosses Surveys, moss samples were taken at 592 sites in 1990, at 1026 sites in 1995 and at 1028 sites in 2000 in Germany. At each of them mosses were sampled and the concentrations of up to 40 metals were measured. The sampling, the ecological characteristics of the sampling sites, and the chemical analysis were documented in a metadata base. An ecoregionalisation of Germany was calculated with data on natural vegetation, altitude, soil texture and climate by means of CART. The ecoregions were mapped by GIS and intersected with multi-metal bioaccumulation indices calculated from the monitoring data 1990, 1995 and 2000. These indices were calculated by percentile statistics and the concentrations of As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, V and Zn were integrated. To reach an integrative exposure assessment, for each survey a CART-model was computed encompassing the multi-metal bioaccumulation indices, the metadata and the ecoregionalization. The models describe the multivariate correlations of metal bioaccumulation with site-specific and ecoregional characteristics in a comprehe

关键词： classification and regression trees (CART) ecoregions GIS metadata multi-metal accumulation indices PEC/PNEC percentile statistics

来源：评论

学校读者我要写书评

暂无评论

Prediction of enantio selectivity using chirality codes and classification and regression trees

引用

ANALYTICA CHIMICA ACTA 2005年第1-2期544卷 315-326页

作者： Caetano, S Aires-De-Sousa, J Daszykowski, M Heyden, YV Free Univ Brussels Dept Pharmaceut & Biomed Anal FABI Laarbeeklaan 103 B-1090 Brussels Belgium Univ Nova Lisboa Fac Ciencias & Tecnol Dept Quim CQFBREQUIMTE P-2829516 Caparica Portugal Silesian Univ Dept Chemometr PL-40006 Katowice Poland

In this paper a new application of classification and regression trees, concerning the prediction of enantioselectivity, is presented. The data consists on the elution order of enantiomers separated by High-Performance Liquid Chromatography with two different chiral stationary phases. The enantiomers of both datasets were classified in two groups, named First and Last, depending on their elution order, prior to the construction of the models. classification and regression trees methodology was then applied to build classification trees that allowed the prediction of the elution order of the compounds by using chirality codes as explanatory variables. The chirality codes are a set of molecular descriptors that combine different parameters and are able to distinguish between enantiomers. This new approach determined quite simple models and achieved good predictions for both datasets considered. Finally the models obtained with classification and regression trees were compared with Kohonen Neural Network results. This methodology was also applied to predict the quality of the separation between two enantiomers in a certain chiral stationary phase. Previously to the construction of the model, the molecules of one of the datasets were classified in three classes (Bad, Good and Very Good), according to their degree of separation (alpha), and the model was built using the absolute values of the chirality codes. The results obtained for the final classification tree were quite promising. (c) 2004 Elsevier B.V. All rights reserved.

关键词： enantioselectivity molecular descriptors chirality codes classification and regression trees chiral stationary phase liquid chromatography

来源：评论

学校读者我要写书评

暂无评论

Bootstrap Aggregating of classification and regression trees in Identification of Single Nucleotide Polymorphisms 9

Bootstrap Aggregating of Classification and Regression Trees...

引用

9th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Hasibuan, Lailan Sabrina Hudachair, Nurul Istiadi, Muhammad Abrar Bogor Agr Univ Dept Comp Sci Fac Math & Nat Sci Bogor Indonesia Bogor Agr Univ Bioinformat Working Grp Fac Math & Nat Sci Bogor Indonesia

ISBN: (纸本)9781538631720

Big data in area of molecular biology has increased rapidly since Next-Generation Sequencing (NGS) technology introduced, a new technology used to sequence DNA with high throughput. Identification of polymorphism in nucleotide is an upstream analysis for some downstream analysis such as producing quality seed based on molecular marker for plant breeding. This paper discusses identification of Single Nucleotide Polymorphism (SNP) underlying NGS data of cultivated soybean (Glycine max L.) using CART (classification and regression Tree). The Identification showed that 51% of true positive SNP could be identified with precision 67%. In order to increase model's performance, Bootstrap Aggregating (bagging) CART was developed with varied number of bootstrap (11, 21, 31, 41, 51, 61, 71, 81, 91). The evaluation indicated that TPR and precision was trade off, when model's TPR was increase the precision one would be decreased. Because of that, F-measure was used as metrics of evaluation. Bagging CART with 51 bootstrap was the best model since it could identify 60% of true positive SNP with precision 66% and F-measure 0.63, while F-measure of model with raw CART was 0.58. The results pointed out that, applying bagging in CART could increase model's performance based on F-measure.

关键词： big data classification and regression trees next generation sequencing single nucleotide polymorphisms

来源：评论

学校读者我要写书评

暂无评论

Using classification and regression trees (CART) to support worker decision making

引用

SOCIAL WORK RESEARCH 2002年第1期26卷 19-29页

作者： Johnson, MA Brown, CH Wells, SJ Child Welf Serv Branch Social Serv div Dept Human Serv Honolulu HI 96813 USA Univ S Florida Coll Publ Hlth Tampa FL 33620 USA Univ Minnesota Sch Social Work St Paul MN 55108 USA

Several approaches can be taken to predict case membership in the classes of a dependent variable. classification and regression trees (CART) analysis has been cited repeatedly as a powerful nonparametric approach in fields where classification or prediction are of concern. To test CART's utility in a social work setting, the authors conducted a secondary analysis of data collected in a national study of child protective services screening practices to identify factors involved with worker decisions to investigate child maltreatment reports. The CART analysis revealed complex interaction effects previously unobserved in the logistic regression. Comparisons of CART with traditional statistical approaches and other tree-based programs are presented.

关键词： classification and regression trees decision trees decision making screening child protective services

来源：评论

学校读者我要写书评

暂无评论

The Application of classification and regression trees Algorithm in the Production Data of Mounter

The Application of Classification and Regression Trees Algor...

引用

International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Zhang Lu Shang Yan-Ling Anyang Radio & TV Univ Dept Comp Sci Anyang 455300 South Korea Anyang Normal Univ Comp Ctr Anyang 455000 Peoples R China

ISBN: (纸本)9783037852958

With the rapid development of database technology and the explosive data growth, we urgently need a new smart technology to help us translate data into useful knowledge and information;so data mining generate. In this paper, We use the classification and regression trees algorithm to mine the data supplied by a factory and acquire some knowledge which can promote the efficiency of the production and reduce the cost. It proved that this method can improve production and reduce the cost efficiency.

关键词： data mining decision tree classification and regression trees

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：