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An NCME Instructional Module on Data Mining Methods for classification and regression

EDUCATIONAL MEASUREMENT-ISSUES AND PRACTICE 2016年第3期35卷 38-54页

作者： Sinharay, Sandip Pacific Metr Corp Monterey CA 93940 USA

Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then demonstrates using three real-data examples that these methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.

关键词： bagging boosting classification and regression tree random forests

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Development and selection of decision trees for water management: Impact of data preprocessing, algorithms and settings

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AI COMMUNICATIONS 2016年第6期29卷 711-723页

作者： Everaert, Gert Pauwels, Ine Bennetsen, Elina Goethals, Peter L. M. Univ Ghent Lab Environm Toxicol & Aquat Ecol Coupure Links 653 B-9000 Ghent Belgium Res Inst Nat & Forest INBO Kliniekstr 25 B-1070 Brussels Belgium

In the present research, we found that different preprocessing options and parameterizations of classification and regression trees alter their model fit and have a direct effect on their applicability for end-users. We found that, in terms of applicability, classification trees react different to pruning than regression trees. Indeed, in case of high pruning levels, classification focus on the extreme values of the response variable, whereas regression tree are more likely to predict the intermediate values. Furthermore, when applying cross-validation with a high number of folds, modellers are likely to find one model that outperforms the other models in terms of reliability. Models were assessed based on the determination coefficient, the percentage of Correctly Classified Instances and the Cohen's Kappa statistic for each parameterization. We found positive correlations (R-2 > 0.70) between the statistical criteria and we found a non-linear negative relation between the model fit and the level of pruning. Therefore, environmental modellers should make use of an exhaustive list of model parameterizations to develop and compare environmental models in a transparent and objective manner. General methodological guidelines derived from the present research may help modellers to efficiently select statistical and ecological relevant models that are meeting the needs of users. The validity of our conclusion should be further tested for other datasets and scientific domains as our findings are based on one set of freshwater data.

关键词： classification and regression tree parameterization applicability field data

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The Knowledge Discovery of the Nuclear Power Issue Using the Artificial Intelligence Model: An Example of the CART and the SVM

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网际网路技术学刊 2016年第6期17卷 1071-1082页

作者： Yueh-Shiun Chung Mei Hsiu-Ching Ho Sun-Jen Huang Shih-Wu Liang

After the nuclear accidents in the Fukushima Daiichi Nuclear Power Plant, the public has become increasingly concerned about nuclear power policies. We employed the CART and the SVM methods to establish and compare classification patterns in order to reveal obscure information in the survey data of Nuclear Power Policy^1. Our results showed that the total classification accuracy of the CART is higher than that of SVM or logistic regression. 48.87% of the Taiwanese public opposed nuclear power, 34.16% supported nuclear power, and 16.97% did not respond to the question. However, the CART analysis showed that among the non-respondents, 56% supported nuclear power and 44% opposed its use. The two primary factors influencing the opinion for nuclear power are ＂satisfaction with the safety of nuclear power＂ and ＂improvement in safety inspection and disaster prevention standards in nuclear power plants.＂ Therefore, by improving the safety of nuclear power plant, the government might regain public support for nuclear power.

关键词： Artificial intelligence classification and regression tree Support vector machine Logistic regression Nuclear power issue

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Evaluation of classification Techniques for Arrhythmia Screening of Astronauts

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Procedia Technology 2016年 24卷 1232-1239页

作者： S. Deepthi Aswathy Ravikumar R. Vikraman Nair Department of Computer Science MBCET Thiruvananthapuram India

Arrhythmia is the major cause of cardiovascular events during space flight. Even though a number of physical tests are conducted to diagnose the disease, in most of the cases the issue remains undetected because of the hidden problems which cannot be pinpointed with regular physical tests. A computation system which can assist in analyzing hidden patterns of physical test is proposed which makes use of data mining and machine learning as the underlying approaches. The present study attempts to evaluate the performance of different individual classifiers such as Naïve Bayes, Support Vector machine (SVM), classification and regression tree (CART), Linear Discriminant Analysis (LDA) and k-nearest neighbor (k-NN). Then the performance of these classifiers is compared with different ensemble techniques such as Majority Voting, Bagging, Dagging and DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples). The performance of the proposed classification methods is analyzed by considering different criteria such as accuracy, sensitivity and specificity. The result shows that among the individual classifiers implemented, k-nearest neighbor is having highest accuracy of around 84.44% only. But Majority Voting, which is an ensemble technique, is having the highest accuracy of 91.11% which is better than the individual classifier.

关键词： Majority Voting Bagging Dagging DECORATE Support Vector Machine K-Nearest Neighbor classification and regression tree Discriminant Analysis Naïve Bayes

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Which Consumers Are Most Responsive to Media-Induced Food Scares?

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Agricultural and Resource Economics Review 2016年第3期38卷 295-310页

作者： Collin R. Payne Kent D. Messer Harry M. Kaiser Marketing at New Mexico State University in Las Cruces New Mexico Food and Resource Economics at University of Delaware in Newark Delaware Applied Economics and Management in the Department of Applied Economics and Management at Cornell University in Ithaca New York

In understanding decreases in demand after exposure to media-induced food scares, aggregate data are almost exclusively presented without taking into consideration potential confounding variables. However, a better approach may be to use an experimental design coupled with targeting homogeneous willingness-to-pay (WTP) subgroups based on similarities in behavioral, psychological, and demographic characteristics of those who are most vulnerable to food scare information. This is accomplished through experimental economics and an analysis strategy called a classification and regression tree (CART). A stigma framework—which guides conceptual understanding of effects of media-induced food scares—suggests controlling contextual variables to better approximate ceteris paribus. To this end, we conducted an experiment that exposed people to information about mad cow disease and then asked them to bid their willingness-to-pay for an actual hamburger. The CART found distinct homogeneous WTP subgroups of individuals that could be used by government and industry professionals to create interventions to reduce potential consumer concern and producer losses.

关键词： food scare media classification and regression tree mad cow disease stigma

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Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第1期63卷 51-59页

作者： Mohktar, Mas S. Redmond, Stephen J. Antoniades, Nick C. Rochford, Peter D. Pretto, Jeffrey J. Basilakis, Jim Lovell, Nigel H. McDonald, Christine F. Univ New S Wales Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ Malaya Dept Biomed Engn Fac Engn Kuala Lumpur 50603 Malaysia Austin Hlth Dept Resp Med Inst Breathing & Sleep Heidelberg Vic 3081 Australia John Hunter Hosp Dept Resp Med Newcastle NSW 2305 Australia Univ Western Sydney Sch Comp & Math Sydney NSW 2751 Australia

Background: The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission. Objective: The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict. the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month. Method: The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit. Results: The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records. Conclusion: The CART analyses have shown that features extracted from three types of physiological measurements;forced expiratory volume in 1 s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth dat

关键词： Home telehealth system classification and regression tree Chronic obstructive pulmonary disease (COPD)

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A Voltage Phasor Based Fault-classification Method for Phasor Measurement Unit Only State Estimator Output

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第1期43卷 22-31页

作者： Gao, Fenghua Thorp, James S. Gao, Shibin Pal, Anamitra Vance, Katelynn A. Southwest Jiaotong Univ Dept Elect Engn Chengdu 610031 Peoples R China Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA USA

Abstract-This article presents a fault-classification method for transmission lines based on voltage phasors using classification and regression trees. The proposed method is intended to aid system operators in understanding the outputs of a phasor measurement unit only state estimator. Faults are classified into four categories when the estimator is positive sequence and into ten categories when the estimator is three phase. The fault data are generated in PowerWorld (R) (PowerWorld Corporation, Champaign, IL, USA) and DSA Tools (R) (Powertech Labs Inc., Surrey, British Columbia, Canada). The pre-fault state consists of a variety of operating conditions and loading angles of faulted lines. The fault condition comprises different fault types, fault locations, fault impedances, and fault incidence angles. Fault classification is done using MATLAB (R) (The MathWorks, Natick, Massachusetts, USA).The approach is successfully tested on the IEEE-118 bus system. The results demonstrate that the technique developed here is effective and robust, irrespective of the pre-fault and fault conditions.

关键词： voltage phasors fault type classification and regression tree fault voltage state estimation phasor measurement units fault classification

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Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2015年 50卷 86-96页

作者： Khan, Ghazan Bill, Andrea R. Noyce, David A. Calif State Univ Sacramento Sacramento CA 95819 USA Univ Wisconsin Traff Operat & Safety TOPS Lab Madison WI 53706 USA

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE classification tree method to analyze the severity of CMCs to discover if any additional information could be revealed. A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE classification trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE classification trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of classification tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. classification tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： classification and regression tree GUIDE Cross-median crashes Crash severity models Multicollinearity

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A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

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WATER RESOURCES MANAGEMENT 2015年第14期29卷 5217-5236页

作者： Naghibi, Seyed Amir Pourghasemi, Hamid Reza Tarbiat Modares Univ Coll Nat Resources Dept Watershed Management Engn Noor Mazandaran Iran Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran

As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. Therefore, the objective of current study is to evaluate the capability of three machine learning models such as boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF), and comparison of their performance by bivariate (evidential belief function (EBF)), and multivariate (general linear model (GLM)) statistical methods in the groundwater potential mapping. This study was carried out in the Beheshtabad Watershed, Chaharmahal-e-Bakhtiari Province, Iran. In total, 1425 spring locations were detected in the study area. Seventy percent of the spring locations were used for model training, and 30 % for validation purposes. Fourteen conditioning-factors were considered in this investigation, including slope angle, slope aspect, altitude, plan curvature, profile curvature, slope length (LS), stream power index (SPI), topographic wetness index (TWI), distance from rivers, distance from faults, river density, fault density, lithology, and land use. Using the above conditioning factors and different algorithms, groundwater potential maps were generated, and the results were plotted in ArcGIS 9.3. According to the results of success rate curves (SRC), values of area under the curve (AUC) for the five models vary from 0.692 to 0.975. In contrast, the AUC for prediction rate curves (PRC) ranges from 77.26 to 86.39 %. The CART, BRT, and RF machine learning techniques showed very good performance in groundwater potential mapping with the AUC values of 86.39, 86.12, and 86.05 %, respectively. By the way, The GLM and EBF models in comparison by machine learning models showed weaker performance in spring groundwater potential mapping by the AUC values of 77.26, and 67.72 %, respectively. The proposed me

关键词： Groundwater potential mapping Boosted regression tree classification and regression tree Random forest General linear model Evidential belief function GIS

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Nonparametric approaches for estimating regional lake nutrient thresholds

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ECOLOGICAL INDICATORS 2015年 58卷 225-234页

作者： Huo, Shouliang Ma, Chunzi Xi, Beidou He, Zhuoshi Su, Jing Wu, Fengchang Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China

Nonparametric approaches including a classification and regression tree (CART), a nonparametric change-point analysis (nCPA) and a Bayesian hierarchical modeling (BHM) method were developed to determine ecoregional nutrient response thresholds. A CART analysis revealed that hierarchical structure was important for predicting Chl a concentrations from total nitrogen (TN) and total phosphorus (TP). The nCPA and BHM methods confirmed the CART results for each node in the tree, and the 90% confidence interval for each threshold was calculated to quantify uncertainty. The CART, nCPA, and BHM methods suggested that the nutrient criteria differed significantly within certain nutrient ecoregions and that numerical nutrient criteria of 0.0150-0.222 mg/L TP and 0.300-1.766 mg/L TN may control Chl a concentrations in the various lake ecoregions. The results of this analysis suggest that the integration of CART, nCPA and BHM might be useful for determining nutrient thresholds. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Nutrient threshold classification and regression tree Nonparametric changepoint analysis Bayesian hierarchical modeling Lake ecoregion

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