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Data mining based framework to identify rule based operation strategies for buildings with power metering system

建筑模拟（英文版） 2019年第2期12卷 195-205页

作者： Shunian Qiu Fan Feng Zhengwei Li Guang Yang Peng Xu Zhenhai Li School of Mechanical Engineering Tongji University Shanghai China

Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it's necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatical y identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatical y. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

关键词： power metering system operation strategy data mining automatic identification weighted association rule mining classification and regression tree

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A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine

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ACTA GEOPHYSICA 2019年第4期67卷 1025-1037页

作者： Hoang Nguyen Xuan-Nam Bui Moayedi, Hossein Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Pho Vien Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Pho Vien Hanoi Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that significantly affect the environment, especially ground vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced ground vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict ground vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected;80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models' performances. Mean absolute error (MAE), determination coefficient (R-2), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced ground vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting ground vibration with an RMSE of 0.508, R-2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878;R-2 of 0.886, 0.618, 0.737;and MAE of 0.659, 1.631, 0.762, respectively.

关键词： Support vector machine classification and regression tree k-nearest neighbor Artificial neural network Ground vibration Open-pit mine

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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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Prediction of the crack condition of highway pavements using machine learning models

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2019年第7期15卷 940-953页

作者： Inkoom, Sylvester Sobanjo, John Barbu, Adrian Niu, Xufeng FAMU FSU Coll Engn Dept Civil & Environm Engn Tallahassee FL 32310 USA Florida State Univ Dept Stat Tallahassee FL 32306 USA

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN;deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree - R-2 > 0.8 and classification tree - R-2 > 0.6) and ANN (continuous response - R-2 > 0.8 and categorical response - R-2 > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).

关键词： Pavement crack prediction recursive partitioning artificial neural network classification and regression tree machine learning infrastructure performance modelling

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Digital mapping of topsoil pH by random forest with residual kriging (RFRK) in a hilly region

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SOIL RESEARCH 2019年第4期57卷 387-396页

作者： Wang, Lei Wu, Wei Liu, Hong-Bin Southwest Univ Coll Resources & Environm Chongqing 400715 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

Soil pH is a vital attribute of soil fertility. The accurate and efficient prediction of soil pH can provide the necessary basic information for agricultural development. In the present study, random forest with residual kriging (RFRK) was used to predict soil pH based on stratum, climate, vegetation and topography in a hilly region. The performance of RFRK was compared with those of the classification and regression tree (CART) and the random forest (RF). Comparative results showed that RFRK provided the best performance. The corresponding values of Lin's concordance correlation coefficient, coefficient of determination, mean absolute error and root mean square error were as follows: 0.70, 0.51, 0.44 and 0.61 for CART;0.80, 0.70, 0.34 and 0.48 for RF;and 0.88, 0.80, 0.25 and 0.39 for RFRK. Stratum and average annual temperature were the most important factors affecting the soil pH in the study area. Results indicate that RFRK is a feasible and reliable tool for predicting soil pH in hilly regions.

关键词： classification and regression tree digital soil mapping soil pH

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Integrated Learning via Randomized Forests and Localized regression With Application to Medical Diagnosis

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IEEE ACCESS 2019年 7卷 18727-18733页

作者： Ogunleye, Adeola Wang, Qing-Guo Marwala, Tshilidzi Univ Johannesburg Fac Engn & Built Environm Inst Intelligent Syst Johannesburg South Africa Univ Johannesburg Fac Engn & Built Environm Johannesburg South Africa

The tree-based machine learning functions on the divide-and-conquer principle and is known to perform well in certain applications. In this paper, we first give a new data partitioning rule using the mean of the data columns to grow the tree till the child nodes are small in size. Then, the local regression is applied to leave nodes to enhance the resolution of the node outputs. Randomization is introduced at tree growth and forest creation. The local prediction accuracies on the leaves are used to select a subset of the test data for actual predictions. The case study on the diagnosis of autistic spectrum disorder shows that the proposed method achieves the prediction accuracy of the ensemble at above 96% with reduced variance, which is much better than those reported in the literature.

关键词： classification and regression tree hybrid expert system decision tree

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Geospatial data mining for digital raster mapping

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GISCIENCE & REMOTE SENSING 2019年第3期56卷 406-429页

作者： Wylie, Bruce K. Pastick, Neal J. Picotte, Joshua J. Deering, Carol A. US Geol Survey Earth Resources Observat & Sci EROS Ctr Sci Div 47914 252 St Sioux Falls SD 57198 USA Stinger Ghaffarian Technol Inc US Geol Survey Earth Resources Observat & Sci EROS Ctr 47914 252 St Sioux Falls SD 57198 USA Univ Minnesota Dept Forest Resources St Paul MN 55108 USA ASRC Fed InuTeq LLC US Geol Survey Earth Resources Observat & Sci EROS Ctr 47914 252 St Sioux Falls SD 57198 USA Innovate Inc US Geol Survey Earth Resources Observat & Sci EROS Ctr 47914 252 St Sioux Falls SD 57198 USA

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or "data mining" trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of "no data" values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.

关键词： Random Forests classification and regression tree CUBIST See5 support vector machines neural networks mapping

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Differential impact of age on survival in head and neck cancer according to classic Cox regression and decision tree analysis

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CLINICAL OTOLARYNGOLOGY 2019年第3期44卷 244-253页

作者： Yang, Ching-Chieh Su, Yu-Chieh Lin, Yu-Wei Huang, Chung-, I Lee, Ching-Chih Chi Mei Med Ctr Dept Radiat Oncol Tainan Taiwan Natl Sun Yat Sen Univ Inst Biomed Sci Kaohsiung Taiwan Chia Nan Univ Pharm & Sci Dept Pharm Tainan Taiwan Kaohsiung Med Univ Hosp Dept Internal Med Div Hematol & Oncol Kaohsiung Taiwan Kaohsiung Med Univ Coll Med Fac Med Kaohsiung Taiwan E Da Canc Hosp Dept Radiat Oncol Kaohsiung Taiwan Kaohsiung Vet Gen Hosp Dept Otolaryngol Head & Neck Surg Kaohsiung Taiwan Natl Yang Ming Univ Inst Hosp & Hlth Care Adm Taipei Taiwan Natl Yang Ming Univ Sch Med Taipei Taiwan Triserv Gen Hosp Dept Otolaryngol Head & Neck Surg Taipei Taiwan

ObjectivesTo assess the impact of age on the survival of patients with head and neck squamous cell carcinoma (HNSCC) using different statistical methods. DesignA retrospective population-based study. SettingSurveillance, Epidemiology, and End Results database. Subjects and methodsA total of 28639 patients with newly diagnosed HNSCC were enrolled between 1 January 2007 and 31 December 2013. The effect of age on 5-year disease-specific survival was calculated using a Kaplan-Meier method and compared using log-rank tests. A Cox proportional hazards model was used for a multivariate analysis. A classification and regression tree (CART) analysis that partitioned patients with significantly different Kaplan-Meier curves was introduced to identify the important cancer-related parameters influencing survival. ResultsUni- and multivariate analyses indicated that patients who were older than 60years had poorer 5-year disease-specific survival regardless of tumour subsite and tumor-node-metastasis (TNM) stage. However, the CART analysis determined that age played only a minor role in survival after comparing with other prognosticators. The relative importance of age using the Gini index was as follows: 3.21% for oral cancer, 8.32% for oropharyngeal cancer, 2.56% for hypopharyngeal cancer and 16.51% for laryngeal cancer. ConclusionsDifferent to traditional statistical methods, the CART analysis which was used to identify homogeneous populations revealed that the impact of age varied for different patient groups according to the presence or absence of other prognosticators. This important information could help to guide our clinical decisions and future researches.

关键词： age classification and regression tree head and neck squamous cell carcinoma regression analysis survival

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Should Sentinel Lymph Node Biopsy Be Performed for All T1b Melanomas in the New 8th Edition American Joint Committee on Cancer Staging System?

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JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS 2019年第4期228卷 466-472页

作者： Egger, Michael E. Stevenson, Megan Bhutiani, Neal Jordan, Adrienne C. Scoggins, Charles R. Philips, Prejesh Martin, Robert C. G., II McMasters, Kelly M. Univ Louisville Dept Surg 315 E BroadwayM-10 Louisville KY 40202 USA Univ Louisville Dept Pathol & Lab Med Louisville KY 40292 USA

BACKGROUND: In the 8th edition of the American Joint Committee on Cancer melanoma staging system, the T1b category has been redefined based solely on thickness and ulceration. National Comprehensive Cancer Network guidelines recommend consideration of sentinel lymph node biopsy (SLNB) for all patients with T1b melanomas (0.8 to 1.0 mm thick). We hypothesized that the new staging system would lead to excessive use of SLNB in patients with non-ulcerated T1b melanomas with a low risk of positive sentinel lymph nodes. STUDY DESIGN: The National Cancer Database 2015 Melanoma Public Use File was used to select patients undergoing SLNB for thin T1 cutaneous melanoma from 2010 to 2015. Clinicopathologic risk factors for having a positive SLNB were evaluated. Univariable and multivariable logistic regression models and classification and regression tree analysis were performed to identify groups with high and low risk of positive SLNB. RESULTS: We selected patients undergoing SLNB without ulceration with thickness 0.75 to 1.04 mm, staged T1b in the new 8th edition American Joint Committee on Cancer by thickness criteria alone (6,894 patients). Independent risk factors for a positive sentinel lymph node were age 56 years or younger (odds ratio [OR] 1.74;95% CI 1.38 to 2.17), thickness 1.0 vs 0.8 to 0.9mm(OR 1.36;95% CI 1.09 to 1.70), female sex (OR1.36;95% CI 1.09 to 1.69), and mitotic rate >= 1/mm(2) (OR 2.01;95% CI 1.54 to 2.64). classification and regression tree analysis identified 2 groups based on age, mitotic rate, and thickness with a risk of positive SLNB <5%. These 2 groups made up 55% of T1b, nonulcerated melanoma patients who underwent SLNB. CONCLUSIONS: The new 8th edition American Joint Committee on Cancer melanoma staging system T1b category should not be used to determine use of SLNB in thin melanoma, as more than one half of T1b lesions without ulceration have a low risk of positive sentinel lymph nodes. (C) 2019 by the American College of Surgeons. Published

关键词： AJCC American Joint Committee on Cancer CART classification and regression tree MR mitotic rate NCDB National Cancer Database SLN sentinel lymph node

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Decision tree -based diagnosis of coronary artery disease: CART model

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 192卷 105400-105400页

作者： Ghiasi, Mohammad M. Zendehboudi, Sohrab Mohsenipour, Ali Asghar Mem Univ Fac Engn & Appl Sci St John NF A1B 3X5 Canada Adv CERT Canada Toronto ON Canada

Background and Objective As the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally. Several diagnosis approaches such as baseline electrocardiography, echocardiography, magnetic resonance imaging, and coronary angiography are suggested for screening the suspected patients that may suffer from CAD. However, applying such methods may have health side effects and/or expensive *** As an alternative to the available diagnosis tools/methods, this research involves a decision tree learning algorithm called classification and regression tree (CART) for a simple and reliable diagnosis of CAD. Several CART models are developed based on the recently CAD dataset published in the *** Utilizing all the features of the dataset (55 independent parameters), it was found that only 40 independent parameters influence the CAD diagnosis and consequently development of the predictive model. Based on the feature importance obtained from the first CART model, three new CART models are then developed using 18, 10, and 5 selected features. Except for the five-feature CART model, the outcomes of developed CART models demonstrate the maximum achievable accuracy, sensitivity, and specificity for CAD diagnosis (100%), while comparing the predictions with the reported targets. The error analysis reveals that the literature models including sequential minimal optimization (SMO), bagging SMO, Naïve Bayes (NB), artificial neural network (ANN), C4.5, J48, Bagging, and ANN in conjunction with the genetic algorithm (GA) do not outperform the CART methodology in classifying patients as normal or *** Hence, the robustness of the tree-based algorithm in accurate and fast predictions is confirmed, implying the proposed classification technique can be successfully utilized to develop a coherent decision-making system for the CAD diagnosis.

关键词： Coronary artery disease classification Decision tree Error analysis classification and regression tree

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