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A data mining based approach for travel time prediction in freeway with non-recurrent congestion

NEUROCOMPUTING 2014年 133卷 74-83页

作者： Li, Chi-Sen Chen, Mu-Chen Natl Chiao Tung Univ Dept Transportat & Logist Management Taipei 100 Taiwan

This study integrates three data mining techniques, K-means clustering, decision trees, and neural networks, to predict the travel time of freeway with non-recurrent congestion. By creating dummy variables and identifying important variables, not only is the prediction performance increased without increasing investment in equipment, but also important variables are obtained concerning the important locations of equipment in order to effectively assist public transit agencies with system maintenance. The experimental results for a segment of 36.1 km of National Freeway No. 1, Taiwan, with non-recurrent congestion show that, whether or not the data generated by the Electronic Toll Collection (etc) system is used as input variables, the travel time prediction method developed in this study is able to improve the prediction performance. Meanwhile, the proposed approach also reduces the percentage of samples with mean absolute percentage error (MAPE) > 20%. Furthermore, in this study, important variables are extracted from the decision tree in order to predict the travel time. Finally, the prediction models constructed in accordance with six scenarios are highly accurate due to the low MAPE values, which are from 6% to 9%. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Travel time prediction Non-recurrent congestion K-means classification and regression tree Neural networks

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Differentiation of honeydew honeys and blossom honeys: a new model based on colour parameters

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2019年第5期56卷 2771-2777页

作者： Bergamo, Greici Seraglio, Siluana Katia Tischer Gonzaga, Luciano Valdemiro Fett, Roseane de Mello Castanho Amboni, Renata Dias Dias, Carolinne Odebrecht Oliveira Costa, Ana Carolina Univ Fed Santa Catarina Dept Food Sci & Technol Florianopolis SC Brazil

The present study aimed to differentiate Mimosa scabrella Bentham (bracatinga) honeydew honeys from blossom honeys, with and without addition of heat treatment, and bracatinga honeydew honeys adulterated with blossom honeys (5, 15 and 25% of blossom honeys), using chromatic characterization associated with chemometric analysis. Bracatinga honeydew honeys presented unusual chromatic characteristics which allowed differentiation of blossom honeys by principal components analysis. Additionally, a classification model was developed in order to establish clear rules that characterize each group of honey. The proposed model correctly classified bracatinga honeydew honey and blossom honey samples, with and without heat treatment. Only two samples adulterated with 5% blossom honey were misclassified. The chromatic analysis associated with chemometric analysis showed promising perspectives for its exploitation being able to be used for screening and selection of bracatinga honeydew honey, fresh or thermally treated as well as fraud detection.

关键词： Data-mining C&RT classification and regression tree Principal component analysis Floral honey Botanical discrimination CIELAB

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A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS CREDIT SCORING MANAGEMENT

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Journal of Systems Science and Systems Engineering 2006年第4期15卷 419-435页

作者： Wenbing XIAO Qian ZHAO Qi FEI Institute of Systems Engineering Huazhong University of Science & Technology Wuhan 430074 China School of Economics Renmin University of China Beijing 100872 China

Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms （e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor）, we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines （SVMs）, classification and regression tree （CART）, and multivariate adaptive regression splines （MARS）. The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS＇s explanatory capability outperforms the other methods.

关键词： Data mining credit scoring classification and regression tree support vector machines,multivariate adaptive regression splines credit-risk evaluation

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Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes

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GEODERMA 2007年第3-4期142卷 285-293页

作者： Minasny, Budiman McBratney, Alex B. Univ Sydney Fac Agr Food & Nat Resources Sydney NSW 2006 Australia

Mapping soil classes digitally generally starts with soil profile description with observed soil classes at a taxonomic level in a particular classification system. At each soil observation location there is a set of co-located environmental variables, and the challenge is to correlate the soil class with the environmental variables. The current methodology treats soil classes as 'labels' and their prediction only considers the minimisation of the misclassification error. Soil classes at any taxonomic level have taxonomic relationships between each other, and in some instances the errors in prediction of certain classes are more serious than the others. No statistical procedure so far has been utilised to account for these relationships. This paper shows that in digital mapping of soil classes, we can incorporate the taxonomic distance between soil classes in a supervised classification routine. Using classification trees, we can specify an algorithm that minimises the taxonomic distance rather than misclassification error. Two examples are given in this paper for mapping soil orders in the Australian soil classification system. A site in the Edgeroi area showed the advantage of using the method that minimises the taxonomic distance. Meanwhile a site in the Hunter Valley showed minimising the misclassification error performed similarly to minimising taxonomic distance. The advantages and challenges of using soil taxonomic distance are discussed. (C) 2007 Elsevier B.V. All rights reserved.

关键词： soil classification spatial prediction decision tree supervised classification classification and regression tree

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The Bayesian Additive classification tree applied to credit risk modelling

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第5期54卷 1197-1205页

作者： Zhang, Junni L. Haerdle, Wolfgang K. Peking Univ Dept Business Stat & Econometr Guanghua Sch Management Beijing 100871 Peoples R China Humboldt Univ Ctr Appl Stat & Econ Wirtschaftswissenschaftliche Fak D-10178 Berlin Germany

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive classification tree (BACT), which extends the Bayesian Additive regression tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive model specified by a prior and a likelihood in which the additive components are trees, and it is fitted by an iterative MCMC algorithm. Each of the trees learns a different part of the underlying function relating the dependent variable to the input variables, but the sum of the trees offers a flexible and robust model. Through several benchmark examples, we show that the BACT shows excellent performance. We apply the BACT technique to classify whether firms would be insolvent. This practical example is very important for banks to construct their risk profile and operate successfully. We use the German Creditreform database and classify the solvency status of German firms based on financial statement information. We show that the BACT is a serious competitor to the logit model, CART, the Support Vector Machine, random forest and gradient boosting. (C) 2010 Published by Elsevier B.V.

关键词： classification and regression tree Financial ratio Misclassification rate Accuracy ratio

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Support vector regression based hybrid rule extraction methods for forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第8期37卷 5577-5589页

作者： Farquad, M. A. H. Ravi, V. Raju, S. Bapi Inst Dev & Res Banking Technol Hyderabad 500057 Andhra Pradesh India Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Support Vector regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM) introduced by Vapnik (1995). The main drawback of these newer techniques is their lack of interpretability. In other words, it is difficult for the human analyst to understand the knowledge learnt by these models during training. The most popular way to overcome this difficulty is to extract if-then rules from SVM and SVR. Rules provide explanation capability to these models and improve the comprehensibility of the system. Over the last decade, different algorithms for extracting rules from SVM have been developed. However rule extraction from SVR is not widely available yet. In this paper a novel hybrid approach for extracting rules from SVR is presented. The proposed hybrid rule extraction procedure has two phases: (1) Obtain the reduced training set in the form of support vectors using SVR (2) Train the machine leaning techniques (with explanation capability) using the reduced training set. Machine learning techniques viz., classification and regression tree (CART), Adaptive Network based Fuzzy Inference System (ANFIS) and Dynamic Evolving Fuzzy Inference System (DENFIS) are used in the phase 2. The proposed hybrid rule extraction procedure is compared to stand-alone CART. ANFIS and DENFIS. Extensive experiments are conducted on five benchmark data sets viz. Auto MPG, Body Fat, Boston Housing, Forest Fires and Pollution, to demonstrate the effectiveness of the proposed approach in generating accurate regression rules. The efficiency of these techniques is measured using Root Mean Squared Error (RMSE). From the results obtained, it is concluded that when the support vectors with the corresponding predicted target values are used, the SVR based hybrids outperform the stand-alone intelligent techniques and also the case when the support vectors with the corresponding actual target values are used. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Rule extraction Support vector machine Support vector regression classification and regression tree Adaptive network based fuzzy inference system Dynamic evolving fuzzy inference system Root mean squared error

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Development of Novel Hybrid Intelligent Predictive Models for Dilution Prediction in Underground Sub-level Mining

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MINING METALLURGY & EXPLORATION 2024年第4期41卷 2079-2098页

作者： Chimunhu, Prosper Faradonbeh, Roohollah Shirani Topal, Erkan Asad, Mohammad Waqar Ali Ajak, Ajak Duany Curtin Univ WA Sch Mines Minerals Energy & Chem Engn Kalgoorlie WA 6430 Australia Dufico Consulting Min & Data Analyt Perth WA Australia

Tenuous dilution estimates in underground mine production scheduling continue to cause significant variations between schedule forecasts and actual production. This arises partly from the inference of dilution from predecessor stopes' performance, disregarding that these stopes would have undergone multiple intermediate design changes between scheduling and actual mining. The resultant drill and blast-influenced dilution factors gradually lose its robustness over longer planning horizons or when applied to greenfield or brownfield expansions that do not have prior performance data. To overcome this problem, a new methodology is proposed to predict dilution in underground sub-level open stoping (SLOS) using basic geological, geotechnical and stope design attributes available in the early stage of mine planning. The method utilises principal component analysis (PCA), classification and regression tree (CART) algorithm and stepwise selection and elimination (SSE) analysis. First, SSE analysis was conducted to identify the most important independent variables to be used with the CART algorithm (i.e., the SSE-CART model) to provide a predictive model. PCA analysis was then performed, and the new principal components were used to propose a new comparative model (i.e., the PCA-CART model). Low R-2 values were observed for both models, necessitating the consolidation of dilution categories to increase the models' prediction bandwidth. The hybrid PCA-CART model outperformed the SSE-CART model with overall F1 score prediction accuracy of 72% and target dilution category prediction accuracy of over 93% against SSE-CART's 70% and 72%, respectively. Importantly, this study revealed a 13% minimum underestimation of dilution relative to the original design stopes.

关键词： Machine learning classification and regression tree Principal component analysis Stope transformation Dilution prediction Underground mining

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Identification of the most limiting factor for rice yield using soil data collected before planting and during the reproductive stage

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LAND DEGRADATION & DEVELOPMENT 2018年第8期29卷 2310-2320页

作者： Wang, Ming-Ming Rengasamy, Pichu Wang, Zhi-Chun Yang, Fu Ma, Hong-Yuan Huang, Li-Hua Liu, Miao Yang, Hao-Yu Li, Jing-Peng An, Feng-Hua Li, Yang-Yang Liu, Xiao-Long Liang, Zheng-Wei Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Jilin Peoples R China Chinese Acad Sci Daan Sod Land Expt Stn Daan 131317 Peoples R China Chinese Acad Sci Key Lab Mollisols Agroecol Changchun 130102 Jilin Peoples R China Univ Adelaide Sch Agr Food & Wine Adelaide SA 5064 Australia

Soil parameters, measured before crop planting, are typically used to quantify the relationship between soil properties and crop yield and to identify factors limiting crop yield. However, soil properties during sensitive stages of crop growth may have a greater effect on crop yield than the initial values. We determined if inclusion of soil properties, measured during the reproductive stage, could improve the accuracy of crop yield prediction. classification and regression trees were used to determine the explanatory power of different soil and rice variables for predicting rice yield in alkaline salt-affected paddy fields in northeast China. The traditional method explained 77.5% of the rice yield variation and identified soil CO32- in the 0- to 10-cm soil layer, with single explanatory power of 53.4%, as the most important predictor. The whole explanatory power of the methods including soil variables during the reproductive stage and yield components, with/without soil variables before planting, increased to 81.3%. The residual sodium carbonate, measured in the 0- to 10-cm soil layer during the reproductive stage, was identified as the most limiting factor due to its single maximum explanatory power of 60.5%. We conclude that inclusion of soil properties, measured during the reproductive stage, has potential for improving the rice yield prediction accuracy by enhancing the explanatory power in identification of the most limiting factor. These results encourage further investigation of the role of soil properties during sensitive stages of crop growth in crop yield prediction under different soil and climatic conditions.

关键词： classification and regression tree residual sodium carbonate rice salt-affected soil soil constraints

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An online compressor liquid floodback fault diagnosis method for variable refrigerant flow air conditioning system

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INTERNATIONAL JOURNAL OF REFRIGERATION 2020年第0期111卷 9-19页

作者： Zhou, Zhenxin Wang, Jiangyu Chen, Huanxin Wei, Wentian Xu, Chengliang Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerant & Cryogen Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol EU Inst Clean & Renewable Energy Sch China Wuhan 430074 Peoples R China

Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the classification and regression tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability. (C) 2019 Elsevier Ltd and IIR. All rights reserved.

关键词： Back-propagation neural network Variable refrigerant flow Compressor liquid floodback Online diagnosis classification and regression tree

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A support vector machine-based model for detecting top management fraud

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KNOWLEDGE-BASED SYSTEMS 2011年第2期24卷 314-321页

作者： Pai, Ping-Feng Hsu, Ming-Fu Wang, Ming-Chieh [a] Department of Information Management National Chi Nan University Taiwan ROC [b] Department of International Business Studies National Chi Nan University Taiwan ROC

Detecting fraudulent financial statements (FFS) is critical in order to protect the global financial market. In recent years, FFS have begun to appear and continue to grow rapidly, which has shocked the confidence of investors and threatened the economics of entire countries. While auditors are the last line of defense to detect FFS, many auditors lack the experience and expertise to deal with the related risks. This study introduces a support vector machine-based fraud warning (SVMFW) model to reduce these risks. The model integrates sequential forward selection (SFS), support vector machine (SVM), and a classification and regression tree (CART). SFS is employed to overcome information overload problems, and the SVM technique is then used to assess the likelihood of FFS. To select the parameters of SVM models, particle swarm optimization (PSO) is applied. Finally, CART is employed to enable auditors to increase substantive testing during their audit procedures by adopting reliable, easy-to-grasp decision rules. The experiment results show that the SVMFW model can reduce unnecessary information, satisfactorily detect FFS, and provide directions for properly allocating audit resources in limited audits. The model is a promising alternative for detecting FFS caused by top management, and it can assist in both taxation and the banking system. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Support vector machine classification and regression tree Feature selection Top management fraud Corporate governance

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