Search

As a fundamental factor driving economic growth, natural capital plays a key role in sustainable development. This paper incorporates various economic growth proxy variables into the MRW model and uses the BMA method to tackle the uncertainty issue of the model. Based on China's provincial-level panel data, this paper examines the impact of renewable natural capital and non-renewable natural capital on the country's economic growth and then carries out multiple mechanism analysis with the CART algorithm. The main findings are: 1 Natural capital has a significant deep impact on China's economic growth in models incorporating proximate and fundamental theories, and the direction of natural capital's impact varies with proxy indicators, which shows a significant 'Curse' effect of dependence and a 'Blessing' effect of abundance. This panoramic perspective can reconcile the serious disagreement on 'Resource Curse' among the literature. 2 The effects of renewable natural capital are in line with the 'Resource neutral' hypothesis, while nonrenewable natural capital has a stronger impact on economic growth in terms of both the 'Curse' effect of dependence and the 'Blessing' effect of abundance, and the overall effect on economic growth depends on patterns of resource use. 3 Under high physical capital accumulation, natural capital has a more fundamental impact on economic growth, and the 'Blessing' effect of abundance has been significantly enhanced. Based on their resource endowments and economic development levels, all provinces should reinvest natural resource rents in national wealth to mitigate the 'Resource Curse' through effective protection of natural capital and compensation from reinvestment. This is a reasonable way to embark on a win-win sustainable development path of economic development and ecological civilization. [ABSTRACT FROM AUTHOR]

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. [ABSTRACT FROM AUTHOR]

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build‐it‐yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree‐building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction. [ABSTRACT FROM AUTHOR]

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build‐it‐yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree‐building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction. [ABSTRACT FROM AUTHOR]

Objectives: Traffic congestion detection is one of the key points and difficulties of urban traffic management. The existing congestion detection methods are based on road sections, which is not conducive to the extraction of spatiotemporal evolution information of congestion. Moreover, most of the detection only involves the degree of congestion but lacks the congestion type identification. With the classification and regression tree (CART) algorithm, this paper proposes a method for the classification and detection of traffic congestion points, which takes the road section point as the detection unit. In the practical application of this method, congestion points and their categories can be detected in real time according to the average running speed on the road section. Methods: Firstly, the road section is divided at a specific interval and mapped to be road section points. According to the abnormal rules and patterns of spatiotemporal road conditions, the spatiotemporal evolution patterns of four congestion types are analyzed with road section points as units. Secondly, the spatial and temporal sequence of road conditions of road section points is extracted on the basis of the road condition detection of road sections, and the spatial and temporal sequence of road conditions is classified and labeled according to different congestion types. Thirdly, four speed indexes are selected to constitute the attribute set of samples, and the speed of each road section point at each period is extracted according to the attribute set, which forms the dataset of decision tree learning. Finally, with the CART algorithm, the optimal model is obtained by the training with cross-validation to achieve the best generalization ability. Results: This paper proposes a classification and detection method of traffic congestion points based on CART. On one hand, congestion point detection is added to refine the basic unit of congestion detection, and on the other hand, congestion point type detection is also involved. Classification and detection of congestion points is helpful to improve the efficiency of traffic management. Conclusions: The proposed method is compared with the support vector machine classification model, and the experimental results show that the method in this paper has higher accuracy, higher recall rate, and better classification and detection timeliness. [ABSTRACT FROM AUTHOR]

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K-means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision-making system based on Fuzzy ID3 and Min-Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min-Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk-oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value. [ABSTRACT FROM AUTHOR]

Galling wear in sheet metal stamping processes can degrade the product quality and adversely affect mass production. Studies have shown that acoustic emission (AE) sensors can be used to measure galling. In the literature, attempts have been made to correlate the AE features and galling wear in the sheet metal stamping process. However, there is very little attempt made to implement machine learning (ML) techniques to detect AE features that can classify non-galling and galling wear as well as provide additional wear-state information in the form of strong visualisations. In the first part of the paper, time domain and frequency domain analysis is used to determine the AE features that can be used for unsupervised classification. Due to galling wear progression on the stamping tools, the behaviour of AE waveform changes from stationary to a non-stationary state. The initial change in AE waveform behaviour due to galling wear initiation is very difficult to observe due to the ratio of change against the large data size of the waveform. Therefore, a time-frequency technique "Hilbert Huang transform" is applied to the AE waveform as that is sensitive to change of wear state and is used for the classification of "non-galling" and the "transition of galling." Also, the unsupervised learning algorithm fuzzy clustering is used as comparison against the supervised learning techniques. Despite not knowing a priori the wear-state labels, fuzzy clustering is able to define three relatively accurate distinct classes: "unworn", "transition to galling" and "severe galling". In the second part of the paper, the AE features are used as an input to the supervised ML algorithms to classify AE features related to non-galling and galling wear. An accuracy of 96% was observed for the prediction of non-galling and galling wear using classification, regression tree (CART) and neural network techniques. In the last part, a reduced short time Fourier transform of top 10 absolute maximum component AE feature sets that correlates to wear measurement data "profile depth" is used to train and test supervised neural network and CART algorithms. The algorithms predicted the profile depth of 530 unseen parts (530 unseen cases), which did not have any associated labelled depth data. This shows the power of using ML techniques that can use a small data training set to provide additional predicted wear state on a much larger data set. Furthermore, the ML techniques presented in this paper can be used further to develop a real-time measurement system to detect the transition of galling wear from measured AE features. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the hourly PM2.5 concentration in the air is constantly changing. In order to improve the accuracy of PM2.5 prediction, a prediction model based on the Classification and Regression Tree (CART) and Broad Learning System (BLS) was constructed. Firstly, the CART algorithm was used to segment the dataset in a hierarchical way to obtain a subset with similar characteristics. Secondly, the BLS model was trained by using the data of each subset, and the validation error of each model was minimized by adjusting the window number of the mapping layer in the BLS network. Finally, for each leaf in the tree, the global BLS model and the local BLS model on the path from the root node to the leaf node are compared, and the model with the smallest error is selected. The data collected in this paper come from the Chine Meteorological Historical Data website. We selected historical data from the Huaita monitoring station in Xuzhou city for experimental analysis, which included air pollutant content and meteorological data. Experimental results show that the prediction effect of the CART–BLS model is better than that of RF, V-SVR, and seasonal BLS models. [ABSTRACT FROM AUTHOR]

Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy. [ABSTRACT FROM AUTHOR]

In order to overcome the problems existing in traditional financial risk evaluation methods, such as high generalization error and fitting degree with actual value, this paper designs a financial risk evaluation method of blockchain digital currency based on CART algorithm. After screening and ranking the financial risk indicators of blockchain digital currency, the financial risk level of blockchain digital currency is determined by combining CART algorithm. Finally, by calculating the covariance matrix of the financial risk decision matrix, the positive ideal solution and the negative ideal solution of the financial risk are found, and then the final evaluation result is obtained by combining the progress of the financial risk. Experimental results show that the minimum generalization error of this method is only 0.021, the fitting degree of the obtained results and the actual risk can reach 98.0%, and the maximum risk accuracy can reach 0.978. [ABSTRACT FROM AUTHOR]

Day‐ahead forecasting for campus‐level load is important for better energy management, but especially difficult to be accurate, compared to large‐scale loads such as cities or regions. This is because irregular and unpredictable behavior of individual loads in small‐scale loads cannot be fully smoothed out and thus pose a negative impact on forecasting accuracy. This paper presents an online refinement strategy for day‐ahead forecasting using intraday data for a campus‐level load, focusing on self‐adapting correction of forecasting considering load pattern. First, according to the load pattern forecasted by Classification and Regression Trees (CART) one day ahead, the correction parameter group is assigned to the correction model. If the correction conditions are met, the forecasting results for the rest of the day will be updated. After all‐day load data is achieved, the actual load pattern can be identified and used to train CART model. To confirm the effectiveness of the proposed strategy, the model is evaluated by two datasets, a real‐life dataset of an industrial park in Shanghai China and a public dataset of an institute campus in Delhi, India. The results demonstrate that the proposed strategy can improve short‐term forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper is intended to apply data mining techniques for real Iraqi biochemical dataset to discover hidden patterns within tests relationships. It is worth noting that preprocessing steps take remarkable efforts to handle this type of data, since it is pure data set with so many null values reaching a ratio of 94.8%, then it becomes 0% after achieving these steps. However, in order to apply Classification And Regression Tree (CART) algorithm, several tests were assumed as classes, because of the dataset was unlabeled. Which then enabled discovery of patterns of tests relationships, that consequently, extends its impact on patients‟ health, since it will assist in determining test values by performing only relevant tests. Therefore decreases the number of tests for patients. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk. [ABSTRACT FROM AUTHOR]

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend. [ABSTRACT FROM AUTHOR]

• Counterfactual sets, a new explanation technique based on counterfactuals. • Counterfactual sets are summarized using an interpretable representation. • A fusion of Random Forest tree predictors into a single Decision Tree. • A method to extract counterfactual sets from a Random Forest. • The extracted counterfactual set contains the optimal counterfactual by design. Nowadays, Machine Learning (ML) models are becoming ubiquitous in today's society, supporting people with their day-to-day decisions. In this context, Explainable ML is a field of Artificial Intelligence (AI) that focuses on making predictive models and their decisions interpretable by humans, enabling people to trust predictive models and to understand the underlying processes. A counterfactual is an effective type of Explainable ML technique that explains predictions by describing the changes needed in a sample to flip the outcome of the prediction. In this paper, we introduce counterfactual sets, an explanation approach that uses a set of counterfactuals to explain a prediction rather than a single counterfactual, by defining a sub-region of the feature space where the counterfactual holds. A method to extract counterfactual sets from a Random Forest (RF), the R a n d o m F o r e s t O p t i m a l C o u n t e r f a c t u a l S e t E x t r a c t o r (R F − O C S E) , is presented. The method is based on a partial fusion of tree predictors from a RF into a single Decision Tree (DT) using a modification of the CART algorithm, and it obtains a counterfactual set that contains the optimal counterfactual. The proposal is validated through several experiments against existing alternatives on ten well-known datasets by comparing the percentage of valid counterfactuals, distance to the factual sample, and counterfactual sets quality. [ABSTRACT FROM AUTHOR]

Introduction: Data mining techniques, including decision tree algorithms can be used for modeling and identifying those at risk of developing COVID-19. The main goal of this study is estimating the risk of death of patients due to COVID-19, using the classification and regression tree (CART) algorithm based on the observed effective factors. Methods: This paper is an analytical study and the data of all patients with COVID-19 registered on the Kaggle site through Johns Hopkins University, was extracted. There was a total of 26,031 records from various countries. Data analysis was performed using JMP statistical software version 13. In the modeling section, decision tree algorithm and CART model were used. Results: The results of the classification and regression tree showed that among quantitative variables, age, the interval between hospitalization and result, the interval between onset of symptoms and test result, and the interval between hospitalization and test result, and the qualitative variable of gender were the most important factors affecting the outcome of patient treatment, respectively. According to the analysis of words, fever, cough, sore throat, fatigue, weakness, headache, chills, and runny nose, respectively, were the most common symptoms among patients with this disease. Conclusion: The accuracy of the fitted model was shown to be 94.1% for experimental data and 91.1% for educational data using the area under the receiver operating characteristic (ROC) curve. [ABSTRACT FROM AUTHOR]

The evaluation of subhealth of traditional college teachers is lack of timeliness, objectivity and efficiency. In the big data environment, the subhealth assessment model can be established more effectively by information technology, so as to support the evaluation and prediction of subhealth state of university teachers. Firstly, according to the analysis of multidimensional subhealth impact factors of university teachers, the paper constructs a decision conceptual model of the impact of multidimensional subhealth, followed by the analysis and data processing of the sample data characteristics, based on the detailed process of using CART algorithm. Finally, the parallel implementation process and experimental verification based on Spark are given. The subhealth concept model of university teachers objectively reflects the evaluation factors of sub health. The decision tree model can support the prediction and analysis of subhealth of university teachers, and experiments verify the validity, immediacy and accuracy of the model. [ABSTRACT FROM AUTHOR]

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets. [ABSTRACT FROM AUTHOR]

To take advantages of magnetic sensor technology in terms of cost, size, weight, power consumption and wireless communication, a wireless multi-functional magnetic sensor was designed and developed. Then, a novel method with single multi-functional magnetic sensor and optimal Minimum Number of Split-sample (MNS)-based Classification and Regression Tree (CART) algorithm was proposed in this paper to classify on-road vehicles. The sensor was deployed on the road to acquire real-time vehicle waveform data. The decision tree model based on CART algorithm was used to execute on-line vehicle classification in the sensor node. Eight speed-independent time-domain waveform features were extracted as the model inputs. This paper trained the decision tree model by using vehicle samples derived from the multi-functional magnetic sensor and pruned the optimal decision tree with a Minimum Error Pruning (MEP) rule to obtain an optimal pruning tree which is more robust to new samples. Some experiments were implemented by different sample sets and classification methods. The results showed that the proposed method achieved on-line vehicle classification in the sensor node. For the field sample sets with two vehicle classes, the average accuracy rates of test samples were 88.9% and 94.4% in the original samples and swapping samples respectively. Besides higher accuracy, the method also has a better sample robustness, which is easy to classify new samples. The comparison results of current methods also showed that the proposed method has some advantages in aspects of accuracy rate, sample robustness and execution time. [ABSTRACT FROM AUTHOR]

Random forests are ensemble learning methods introduced by Breiman [Mach. Learn. 45 (2001) 5–32] that operate by averaging several decision trees built on a randomly selected subspace of the data set. Despite their widespread use in practice, the respective roles of the different mechanisms at work in Breiman's forests are not yet fully understood, neither is the tuning of the corresponding parameters. In this paper, we study the influence of two parameters, namely the subsampling rate and the tree depth, on Breiman's forests performance. More precisely, we prove that quantile forests (a specific type of random forests) based on subsampling and quantile forests whose tree construction is terminated early have similar performances, as long as their respective parameters (subsampling rate and tree depth) are well chosen. Moreover, experiments show that a proper tuning of these parameters leads in most cases to an improvement of Breiman's original forests in terms of mean squared error. [ABSTRACT FROM AUTHOR]

This paper deals with an application of the notion of barrier in mixed constrained nonlinear systems to an example of a pendulum mounted on a cart with non-rigid cable, whose dynamics may switch to free-fall when the tension of the cable vanishes. We present a direct construction of the boundary of the potentially safe set in which there always exists a control such that the cable never goes slack. A discussion on the dependence of this set with respect to the pendulum and cart masses is then sketched. [ABSTRACT FROM AUTHOR]

Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for supervised water-body extraction and water-body types identification from Radarsat-2 fully polarimetric (FP) synthetic aperture radar (SAR) data in complex urban areas. First, supervised water-body extraction using the Wishart classifier is performed, and the false alarms that are formed in built-up areas are removed using morphological processing methods and spatial contextual information. Then, the support vector machine (SVM), the classification and regression tree (CART), TreeBagger (TB), and random forest (RF) classifiers are introduced for water-body types (rivers, lakes, ponds) identification. In SAR images, certain other objects that are misclassified as water are also considered in water-body types identification. Several shape and polarimetric features of each candidate water-body are used for identification. Radarsat-2 PolSAR data that were acquired over Suzhou city and Dongguan city in China are used to validate the effectiveness of the proposed method, and the experimental results are evaluated at both the object and pixel levels. We compared the water-body types classification results using only shape features and the combination of shape and polarimetric features, the experimental results show that the polarimetric features can eliminate the misclassifications from certain other objects like roads to water areas, and the increasement of classification accuracy embodies at both the object and pixel levels. The experimental results show that the proposed methods can achieve satisfactory accuracies at the object level [89.4% (Suzhou), 95.53% (Dongguan)] and the pixel level [96.22% (Suzhou), 97.95% (Dongguan)] for water-body types classification, respectively. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation. [ABSTRACT FROM AUTHOR]

Large building stocks should be well managed, in terms of ordinary activities and formulating strategic plans, to achieve energy savings through increased efficiency. It is becoming extremely important to have the capability to quickly and reliably estimate buildings’ energy consumption, especially for public authorities and institutions that own and manage large building stocks. This paper analyses the heating energy consumption of eighty school buildings located in the north of Italy. Two estimation models are developed and compared to assess energy consumption: a Multiple Linear Regression (MLR) model and a Classification and Regression Tree (CART). The CART includes interpretable decision rules that enable non-expert users to quickly extract useful information to benefit their decision making. The output of MLR model is an equation that accounts for all of the major variables affecting heating energy consumption. Both models were compared in terms of Mean Absolute Error (MAE), Root Mean Square error (RMSE), and Mean Absolute Percentage error (MAPE). The analysis determined that the heating energy consumption of the considered school buildings was mostly influenced by the gross heated volume, heat transfer surfaces, boiler size, and thermal transmittance of windows. [ABSTRACT FROM AUTHOR]

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. [ABSTRACT FROM AUTHOR]

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively. [ABSTRACT FROM AUTHOR]

Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset. [ABSTRACT FROM AUTHOR]

Abstract: From the view of a cell, the partition of a pattern space is a uniform partition. It is difficult to meet the needs of spatial non-uniform partitioning. In this paper, a cellular automaton classifier with a tree structure is proposed, by combining multiple-attractor cellular automata with the algorithm CART. The method of construction of the characteristic matrix of the multiple-attractor cellular automata is studied on the basis of particle swarm optimization. This method builds multiple-attractor cellular automata as tree nodes. This kind of classifier can be used to solve the non-uniform partition problem and obtain a good classification performance by using a pseudo-exhaustive field with a few bits, and so can restrain the over-fitting. The feasibility and the effectiveness of this method have been verified by experiments. [Copyright &y& Elsevier]

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios. [ABSTRACT FROM AUTHOR]

Abstract: More than 50 different in vitro and in silico methods assessing specific organ- and system-toxicity, such as haemato-, neuro-, nephro- and hepatotoxicity, as well as intestinal absorption, distribution and metabolism, have been used in the first phase of the ACuteTox project to test a common set of 57 chemicals. This paper describes the methods used for statistical evaluation of concentration–response data collected for each of the endpoint assays, and for the development of a testing strategy applicable for acute toxicity classification of chemicals based on the achieved results of the concentration–response analysis. A final list of in vitro test methods considered to be promising candidates for building blocks of the testing strategy is presented. Only these selected test methods were further investigated in the prevalidation phase of the project. The test methods were chosen according to their reproducibility and reliability and most importantly, according to their potential to classify chemicals into the official acute oral toxicity categories of the EU Classification, Labelling and Packaging (CLP) Regulation. The potential of the test methods to correctly classify the chemicals was assessed by Classification and Regression Trees (CART) analysis. [Copyright &y& Elsevier]

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process. [ABSTRACT FROM AUTHOR]

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results. [ABSTRACT FROM AUTHOR]