Quick, Unbiased, And Efficient Statistical Tree Research Articles

Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average

Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.

Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.

Predictors of length of stay in the coronary care unit in patient with acute coronary syndrome based on data mining methods

IntroductionAssessing the possibility of patient discharge based on data-mining models is one of the common, user-friendly approaches to optimally exploit the limited capacity of hospital beds. ObjectiveThe aim of this study was to determine the predictors of length of stay (LOS) in cardiologic care wards developed and carried out based on data-mining approaches. MethodsData from 136 patient records were evaluated using data-mining analysis approaches including the Multilayer perceptron artificial neural network (MLP-ANN),Quick unbiased and efficient statistical tree (QUEST), support vector machines (SVM), classification and regression tree (CRT), Advanced decision tree (C5.0), Auto Classifier (AC) and Logistic Regression models. ResultsThe median and mean LOS was 4 and 4.15 days (95% CI [3.99, 4.30]), respectively. Predictors are associated with increase in LOS (more than 4 days) were: the ST segment elevation myocardial infarction (STEMI) diagnosis at the time of referral, being in the 50–70 years old group, history of smoking, high blood lipids, history of hypertension, hypertension at the time of admission, and high serum troponin levels. ConclusionUsing classical models to explain the predictors of aoutcome is inefficient when the number of predictors is high and sample size is low. Therefore, the analysis based on new data-mining approaches is a desirable alternative solution. Behavioral factors, especially smoking, are among the important factors in determining the long-term stay in the heart care ward.

Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China

Aeolian desertification has become one of the most serious environmental and socioeconomic problems facing the world today. Quantitative remote sensing technology is an important means to achieve the development trends of aeolian desertified land (ADL). To compensate for the shortcomings in the time scale of Landsat Thematic Mapper and other high-spatial-resolution remote sensing data, this study introduces Moderate Resolution Imaging Spectroradiometer time series data and products to invert the monitoring indicators of ADL. The QUEST (quick, unbiased, and efficient statistical tree) classification method was used to establish the extraction model of ADL based on multiple indicators. The ADL time series dataset was extracted from 2000 to 2015, and the characteristics of ADL and its spatial-temporal dynamics were analyzed. These results were combined with meteorological data and socioeconomic statistics to discuss the main factors influencing ADL. The results showed that, by the end of 2015, the total area of ADL was 32,633 km2, accounting for 26.02% of the study area. The slight, moderate, severe, and extremely severe ADL accounted for 51.39%, 34.11%, 10.31%, and 4.20%, respectively. The total area of ADL decreased significantly at a rate of 2388.60 km2 y−1 from 2000 to 2015. The decreasing area was dominated by the slight and moderate ADL. The reversal of ADL exhibited significant correlations with an increase of annual precipitation and a decrease of annual maximum wind velocity (p < 0.01). The impact of annual maximum wind velocity on ADL is more important than annual precipitation. Increases in population density and the number of livestock did not promote the development of ADL. A series of ecological protection projects and policies created advantageous conditions for the reversal of ADL. This research provides a new method for monitoring ADL and useful information for controlling and managing aeolian desertification in this region.

Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea

We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.

Application of Decision-Tree Model to Groundwater Productivity-Potential Mapping

For the sustainable use of groundwater, this study analyzed groundwater productivity-potential using a decision-tree approach in a geographic information system (GIS) in Boryeong and Pohang cities, Korea. The model was based on the relationship between groundwater-productivity data, including specific capacity (SPC), and its related hydrogeological factors. SPC data which is measured and calculated for groundwater productivity and data about related factors, including topography, lineament, geology, forest and soil data, were collected and input into a spatial database. A decision-tree model was applied and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The resulting groundwater-productivity-potential (GPP) maps were validated using area-under-the-curve (AUC) analysis with the well data that had not been used for training the model. In the Boryeong city, the CHAID and QUEST algorithms had accuracies of 83.31% and 79.47%, and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 86.18% and 80.00%. As another validation, the GPP maps were validated by comparing the actual SPC data. As the result, in the Boryeong city, the CHAID and QUEST algorithms had accuracies of 96.55% and 94.92% and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 87.88% and 87.50%. These results indicate that decision-tree models can be useful for development of groundwater resources.

Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ2) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events.

A model for personnel selection with a data mining approach: A case study in a commercial bank

Orientation: The success or failure of an organisation has a direct relationship with how its human resources are employed and retained.Research purpose: In this paper, a decision-making tool is provided for managers to use during the recruitment process. The effective factors in employees’ performance will be identified by discovering covert patterns of the relationship between employees’ test scores and their performance at work.Motivation for the study: Large amounts of information and data on entrance evaluations and processes have been kept in organisations. There is a need to discover the pattern in the relationship between employee’s test scores and their performance at work as a tool for use during the recruitment process.?Research design, approach and method: The data mining technique that was used in this project serves as the decision tree. Rules derivation was accomplished by the Quick Unbiased and Efficient Statistical Tree(QUEST), Chi-squared Automatic Interaction Detector (CHAID),C5.0 and Classification And Regression Tree (CART) algorithm. The objective and the appropriate algorithm were determined based on seemingly ‘irrelevant’ components, which the Commerce Bank Human Resources management’s experts describe.Main finding: It was found that the ‘performance assessment’ variable was not considered as the objective. Also, it was concluded that out of 26 effective variables only five variables, such as province of employment, education level, exam score, interview score and work experience, had the most effect on the ‘promotion score’ target.Practical/managerial implication: The database and personnel information of the Commerce Bank of Iran (in 2005 and 2006) was studied and analysed as a case study in order to identify the labour factors that are effective in job performance. Appropriate and scientific employment of staff that were selected from the entrance exams of companies and organisations were of crucial importance.Contribution/value-add: It is of great importance that an extensive use of data mining techniques be applied in other management areas. Whilst this is a low-cost technique, it can help managers to discover covert knowledge in their organisations.

Comparison of multilabel classification models to forecast project dispute resolutions

Early forecasting of project dispute resolutions (PDRs) provides decision-support information for resolving potential procurement problems before a dispute occurs. This study compares the performances of classification and ensemble models for predicting dispute handling methods in public–private partnership (PPP) projects. Model analyses use machine learners (i.e., Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Tree-augmented Naïve (TAN) Bayesian), classification and regression-based techniques (i.e., Classification and Regression Tree (CART), Quick, Unbiased and Efficient Statistical Tree (QUEST), Exhaustive Chi-squared Automatic Interaction Detection (Exhaustive CHAID), and C5.0), and combinations of these techniques that performed best for a set of PPP data. Analytical results exhibit that the combined technique of QUEST+CHAID+C5.0 has the best classification accuracy at 84.65% in predicting dispute resolution outcomes (i.e., mediation, arbitration, litigation, negotiation, administrative appeals or no dispute occurred). Moreover, as the dispute category and phase in which the dispute occurs are known during project execution, the best classification model is the CART model, with an accuracy of 69.05%. This study demonstrates effective classification application for early PDR prediction related to public infrastructure projects.

Prediction of prostate cancer using decision tree algorithm

Serum Prostate Specific Antigen (PSA) level is used for prediction of cancer, but this approach suffers from weak sensitivity and specificity. We applied binary-split decision tree (DT) algorithm to prostate cancer prediction problem. Materials and methods: Quick, Unbiased and Efficient Statistical Tree (QUEST) algorithm was used in 750 patients who had a serum PSA levels between 0 and 10 ng/mL. Results: The analysis indicated that following five nodes had different levels of cancer possibility: (1) PSA > 5.98 ng/mL; (2) PSA 0.81; (4) PSA Keywords: