scholarly journals Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes

2019 ◽  
Vol 11 (1) ◽  
pp. 100 ◽  
Author(s):  
Dyah R. Panuju ◽  
David J. Paull ◽  
Bambang H. Trisasongko
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Regression Tree ◽  
Google Earth ◽  
Variable Importance ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Change Analysis ◽  
Extreme Gradient Boosting

This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for post-classification change analysis. The accuracy of change detection was iteratively evaluated based on thresholds composed of mean and a range constant of standard deviation. Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected texture features. Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN), Bagged Multivariate Adaptive Regression Spline (MAB), Regularised Random Forest (RFG), Original Random Forest (RFO), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGB). The best accuracy was 98.8%, which resulted from thresholding MAD variate-2 with constants at 1.7. The highest improvement of classification accuracy was obtained by amending the grey level co-occurrence matrix (GLCM) texture. The identification of variable importance (VI) confirmed that selected GLCM textures (mean and variance of HH or HV) were equally superior, while the contribution of index and decomposition were negligible. The best model produced similar classification accuracy at about 90% for both years 2007 and 2010. Tree-based algorithms including RFO, RFG and XGB were more robust than SVM and ENN. Subtle changes indicated by binary change analysis were somewhat hidden in post-classification analysis. Reclassification by combining all important variables and adding five classes to include subtle changes assisted by Google Earth yielded an accuracy of 82%.

scholarly journals Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

2020 ◽  
Vol 12 (12) ◽  
pp. 1973
Author(s):  
Alim Samat ◽  
Erzhu Li ◽  
Wei Wang ◽  
Sicong Liu ◽  
Cong Lin ◽  
...  
Keyword(s):  
Random Forest ◽  
Image Classification ◽  
Classification Accuracy ◽  
Regression Tree ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

scholarly journals Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches

2021 ◽  
Vol 27 (4) ◽  
pp. 279-286
Author(s):  
Atakan Başkor ◽  
Yağmur Pirinçci Tok ◽  
Burcu Mesut ◽  
Yıldız Özsoy ◽  
Tamer Uçar
Keyword(s):  
Machine Learning ◽  
Regression Tree ◽  
Cost Effective ◽  
Pharmaceutical Formulation ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Disintegration Time ◽  
Pharmaceutical Dosage Form ◽  
Extreme Gradient Boosting

Objectives: Orally disintegrating tablets (ODTs) can be utilized without any drinking water; this feature makes ODTs easy to use and suitable for specific groups of patients. Oral administration of drugs is the most commonly used route, and tablets constitute the most preferable pharmaceutical dosage form. However, the preparation of ODTs is costly and requires long trials, which creates obstacles for dosage trials. The aim of this study was to identify the most appropriate formulation using machine learning (ML) models of ODT dexketoprofen formulations, with the goal of providing a cost-effective and timereducing solution.Methods: This research utilized nonlinear regression models, including the k-nearest neighborhood (k-NN), support vector regression (SVR), classification and regression tree (CART), bootstrap aggregating (bagging), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods, as well as the t-test, to predict the quantity of various components in the dexketoprofen formulation within fixed criteria.Results: All the models were developed with Python libraries. The performance of the ML models was evaluated with R2 values and the root mean square error. Hardness values of 0.99 and 2.88, friability values of 0.92 and 0.02, and disintegration time values of 0.97 and 10.09 using the GBM algorithm gave the best results.Conclusions: In this study, we developed a computational approach to estimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was found that they complied with Food and Drug Administration criteria.

scholarly journals APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES

2021 ◽  
pp. 1-33
Author(s):  
Stéphane Loisel ◽  
Pierrick Piette ◽  
Cheng-Hsien Jason Tsai
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Regression Tree ◽  
Classification Problem ◽  
Point Of View ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Extreme Gradient Boosting

Abstract Modeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

scholarly journals Machine learning as a successful approach for predicting complex spatio–temporal patterns in animal species abundance

2021 ◽  
pp. 289-301
Author(s):  
B. Martín ◽  
J. González–Arias ◽  
J. A. Vicente–Vírseda
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Animal Species ◽  
Temporal Patterns ◽  
Additive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Spatio Temporal

Our aim was to identify an optimal analytical approach for accurately predicting complex spatio–temporal patterns in animal species distribution. We compared the performance of eight modelling techniques (generalized additive models, regression trees, bagged CART, k–nearest neighbors, stochastic gradient boosting, support vector machines, neural network, and random forest –enhanced form of bootstrap. We also performed extreme gradient boosting –an enhanced form of radiant boosting– to predict spatial patterns in abundance of migrating Balearic shearwaters based on data gathered within eBird. Derived from open–source datasets, proxies of frontal systems and ocean productivity domains that have been previously used to characterize the oceanographic habitats of seabirds were quantified, and then used as predictors in the models. The random forest model showed the best performance according to the parameters assessed (RMSE value and R2). The correlation between observed and predicted abundance with this model was also considerably high. This study shows that the combination of machine learning techniques and massive data provided by open data sources is a useful approach for identifying the long–term spatial–temporal distribution of species at regional spatial scales.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

scholarly journals Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3170
Author(s):  
Zemeng Fan ◽  
Saibo Li ◽  
Haiyan Fang
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Regression Tree ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Driving Mechanisms

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.

A Data-Analytics Tutorial: Building Predictive Models for Oil Production in an Unconventional Shale Reservoir

SPE Journal ◽  
2018 ◽  
Vol 23 (04) ◽  
pp. 1075-1089 ◽  
Author(s):  
Jared Schuetter ◽  
Srikanta Mishra ◽  
Ming Zhong ◽  
Randy LaFollette (ret.)
Keyword(s):  
Predictive Models ◽  
Decision Rules ◽  
Regression Tree ◽  
Production Performance ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Well Completion

Summary Considerable amounts of data are being generated during the development and operation of unconventional reservoirs. Statistical methods that can provide data-driven insights into production performance are gaining in popularity. Unfortunately, the application of advanced statistical algorithms remains somewhat of a mystery to petroleum engineers and geoscientists. The objective of this paper is to provide some clarity to this issue, focusing on how to build robust predictive models and how to develop decision rules that help identify factors separating good wells from poor performers. The data for this study come from wells completed in the Wolfcamp Shale Formation in the Permian Basin. Data categories used in the study included well location and assorted metrics capturing various aspects of well architecture, well completion, stimulation, and production. Predictive models for the production metric of interest are built using simple regression and other advanced methods such as random forests (RFs), support-vector regression (SVR), gradient-boosting machine (GBM), and multidimensional Kriging. The data-fitting process involves splitting the data into a training set and a test set, building a regression model on the training set and validating it with the test set. Repeated application of a “cross-validation” procedure yields valuable information regarding the robustness of each regression-modeling approach. Furthermore, decision rules that can identify extreme behavior in production wells (i.e., top x% of the wells vs. bottom x%, as ranked by the production metric) are generated using the classification and regression-tree algorithm. The resulting decision tree (DT) provides useful insights regarding what variables (or combinations of variables) can drive production performance into such extreme categories. The main contributions of this paper are to provide guidelines on how to build robust predictive models, and to demonstrate the utility of DTs for identifying factors responsible for good vs. poor wells.

scholarly journals Modelos de machine learning para predição do sucesso de startups

2021 ◽  
Vol 12 (2) ◽  
pp. 28-55
Author(s):  
Fabiano Rodrigues ◽  
Francisco Aparecido Rodrigues ◽  
Thelma Valéria Rocha Rodrigues
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Initial Public Offering ◽  
Gradient Boosting ◽  
Support Vector ◽  
Trade Offs ◽  
Extreme Gradient Boosting ◽  
Public Offering

Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.   

scholarly journals Research on dairy products detection based on machine learning algorithm

2022 ◽  
Vol 355 ◽  
pp. 03008
Author(s):  
Yang Zhang ◽  
Lei Zhang ◽  
Yabin Ma ◽  
Jinsen Guan ◽  
Zhaoxia Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Electronic Nose ◽  
Milk Fat ◽  
Dairy Products ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

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