Characterization of large reservoir models with a great number of uncertain parameters is frequently carried out by Ensemble-based assimilation Methods, due to their computational efficiency, ease of implementation, versatility, and non-necessity of adjoint code. In this study, multiple Ensemble-based assimilation techniques are utilized to characterize the well-known PUNQ-S3 model. Accordingly, actual measurements are employed to determine porosity, horizontal and vertical permeabilities, and their associated uncertainties. In consequence, the uncertain parameters of the model will gradually be adapted toward the true values during the assimilation of actual measurements, including bottomhole pressure and production rates of the reservoir. Monotonic reduction of root-mean-squared error and capturing the key points of the maps (such as direction of anisotropy and porosity/permeability contrasts) verify successful estimation of the geostatistical properties of the PUNQ-S3 model during history matching. At the end of the assimilation process, the RMSE values for Deterministic Ensemble Kalman Filter, Ensemble Kalman Filter, Ensemble Kalman Filter with Bootstrap Regularization, Ensemble Transform Kalman Filter Symmetric Solution, Ensemble Transform Kalman Filter Random Rotation, and Singular Evolutive Interpolated Kalman filter are 1.120, 1.153, 1.132, 1.132, 1.129, and 1.113, respectively. In addition to RMSE, the quality of history match as well as prediction of future performance are looked into in order to assess the performance of the assimilation process. Obviously, the results of the Ensemble-based assimilation Methods closely match the true results both in the history match section and in the future prediction section. Besides, the uncertainty of future predictions is quantified using multiple history-matched realizations. This is due to the fact that Kalman-based filters use a Bayesian framework in the assimilation step. Accordingly, the updated Ensemble members are samples of the posterior distribution through which the uncertainty of future performance is assessed.

An emerging technique to improve classification performance is to build several different classifiers, and then to combine them, known as multiple classifier systems or Ensemble classification systems. The design process of an Ensemble system generally involves two steps: the collection of an Ensemble of classifiers and the design of the combination rule. Researchers in various fields including pattern recognition, machine learning and statistics have examined the use of Ensemble systems. Nabavi-Kerizi and Kabir provided a review of Ensemble classification, where combining techniques have been mainly considered. However, the trend of recent papers in this active field shows that the Ensemble systems have focused on different ways to design the Ensemble of classifiers. In this paper, first we aim to establish a framework for different approaches. Based on this architecture, each approach has been introduced in details. Combination Methods are then described in brief. At the end, active research areas in the field of Ensemble learning are presented.

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, Ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibration signals and are collected using experimental test rig for different input parameters like load, speed and bearing conditions. These features are ranked using two techniques, namely Decision Tree (DT) and Randomized Lasso (R Lasso), which are further used to form training and testing input feature sets to machine learning techniques. It uses three Ensemble machine learning techniques for AFB fault classification namely Random Forest (RF), Gradient Boosting Classifier (GBC) and Extra Tree Classifier (ETC). The impact of number of ranked features and estimators have been studied for Ensemble techniques. The result showed that the classification efficiency is significantly influenced by the number of features but the effect of number of estimators is minor. The demonstrated Ensemble techniques give more accuracy in classification as compared to tuned SVM with same experimental input data. The highest AFB fault classification accuracy 98. 12% is obtained with ETC and DT feature ranking.

Introduction: In recent years, the infertility ratio in young couples has been increased a lot in Iran. From the other side, it has been shown that data mining techniques are capable of extracting novel patterns from medical data. In this study, we proposed a comprehensive system called Prediction of the best Infertility treatment using Outlier Detection and Ensemble Methods (PIODEM) for predicting of the best infertility treatment method for infertile couples. Methods: This descriptive-correlation study used the information of 527 infertile couples, which collected from Avicenna specialized infertility center, Tehran, Iran. PIODEM consists of three steps: First, PIODEM uses the discriminant analysis to find effective factors for choosing the best infertility treatment. Second, PIODEM detects the outlier samples, and applies a correlation between these samples and the choice of treatment method. Third, it uses Ensemble Methods to increase the precision of classifiers. Results: The PIODEM system succeeded in discovering affective factors such as male-partner’ s age, infertility duration, immotile sperm, decreasing of sperm concentration decrease, total sperm count, morphology, sperm motility, sperm with rapid progressive-a motility, and sperm with slow progressive-b motility. Additionally, PIODEM indicates that if one of four features of sperm concentration, toxoplasma immunoglobulin M (IgM), triiodothyronine (T3) hormone, and thyroid peroxidase (TPO) was an outlier, then the prediction of treatment would be more accurate. Finally, using Ensemble Methods increased the F-measure of PIODEM system by up to 76%. Conclusion: The PIODEM system is able to discover effective factors in the choice of treatment method, using differential analysis and analysis of pert data. This system offers patient information as input for the treatment method.

BACKGROUND AND OBJECTIVES: The healthcare insurance industry faces a significant challenge predicting individuals' insurance costs, which are based on complex parameters such as age and physical characteristics. Insurance companies categorize policyholders into high-risk and low-risk groups to manage risks and avoid potential losses. However, the accurate estimation of costs for each individual can be a daunting task. By leveraging data science and machine learning techniques, insurance companies can improve their cost estimation accuracy and better manage risks. This approach can help insurance companies to provide more accurate insurance coverage and pricing for individuals leading to higher customer satisfaction and lower financial losses.Methods: To address this challenge, a data science and machine learning-based approach that uses Ensemble learning to predict high-risk and low-risk individuals is used. The method involves several steps including data preprocessing, feature engineering, and cross-validation to evaluate the model's performance. The first step involves preprocessing the data by cleaning it, handling missing values, and encoding categorical variables. The second step generates new features using feature engineering techniques such as scaling, normalization, and dimensionality reduction. Next, Ensemble learning is used to combine multiple regression Methods such as logistic regression, neural networks, support vector machines, random forests, LightGBM, and XGBoost. By combining these Methods, the aim is to leverage their strengths and minimize their weaknesses to achieve better prediction accuracy. Finally, the model's performance is evaluated using cross-validation techniques such as k-fold cross-validation. These techniques help to validate the model's accuracy and prevent overfitting.FINDINGS: The proposed approach achieves an AUC of 0.73 demonstrating its effectiveness in predicting high-risk and low-risk individuals.CONCLUSION: In conclusion, the healthcare insurance industry can benefit greatly from data science and machine learning-based approaches. By accurately predicting high-risk and low-risk individuals, insurance companies can better manage risks and provide more accurate coverage and pricing for their customers. This can lead to the improvement of  customer satisfaction and the reduction of financial losses for insurance companies.

Negative Correlation Learning (NCL) and Mixture of Experts (ME), two popular combining Methods, each employ different special error functions for the simultaneous training of NN experts to produce negatively correlated NN experts. In this paper, we review the properties of the NCL and ME Methods, discussing their advantages and disadvantages. Characterization of both Methods showed that they have different but complementary features, so if a hybrid system can be designed to include features of both NCL and ME, it may be better than each of its basis approaches. In this study, an approach is proposed to combine the features of both Methods, i.e., Mixture of Negatively Correlated Experts (MNCE). In this approach, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to establish better balance in bias-variance-covariance trade-offs. The proposed hybrid Ensemble Methods, MNCE, are compared with their constituent Methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed method preserve the advantages and alleviate the disadvantages of their basis approaches, offering significantly improved performance over the original Methods.

As we know, credit cards speed up and make life easier for all citizens and bank customers. They can use it anytime and anyplace according to their personal needs, instantly and quickly and without hassle, without worrying about carrying a lot of cash and more security than having liquidity. Together, these factors make credit cards one of the most popular forms of online banking. This has led to widespread and increasing use for easy payment for purchases made through mobile phones, the Internet, ATMs, and so on. Despite the popularity and ease of payment with credit cards, there are various security problems, increasing day by day. One of the most important and constant challenges in this field is credit card fraud all around the world. Due to the increasing security issues in credit cards, fraudsters are also updating themselves. In general, as a field grows in popularity, more fraudsters are attracted to it, and this is where credit card security comes into play. So naturally, this worries banks and their customers around the world. Meanwhile, financial information acts as the main factor in market financial transactions. For this reason, many researchers have tried to prioritize various solutions for detecting, predicting, and preventing credit card fraud in their research work and provide essential suggestions that have been associated with significant success. One of the practical and successful Methods is data mining and machine learning. In these Methods, one of the most critical parameters in fraud prediction and detection is the accuracy of fraud transaction detection. This research intends to examine the Gradient Boosting Methods, which are a subset of Ensemble Learning and machine learning Methods. By combining these Methods, we can identify credit card fraud, reduce error rates, and improve the detection process, which in turn increases efficiency and accuracy. This study compared the two algorithms LightGBM and XGBoost, merged them using simple and weighted averaging techniques, and then evaluate the models using AUC, Recall, F1-score, Precision, and Accuracy. The proposed model provided 95. 08, 90. 57, 89. 35, 88. 28, and 99. 27, respectively, after applying feature engineering and using the weighted average approach for the mentioned validation parameters. As a result, function engineering and weighted averaging significantly improved prediction and detection accuracy.