Application Of Ensemble Methods Research Articles

Theoretical investigations on the purification of petroleum using desulfurization process: Analysis and optimization of process

This paper introduces a comprehensive approach for predicting chemical concentrations (C) of sulfur compound in a two-dimensional space (x, y) by numerical solution of mass transfer equation and integration of machine learning. Data of training/validation are obtained from mass transfer modeling on adsorption separation utilizing porous adsorbent for petroleum desulfurization. Mass transfer modeling data were utilized for machine learning models which included three different regression models, namely Support Vector Machine (SVM), Decision Tree Regression (DT), and Multilayer Perceptron (MLP). The hyper-parameters of these base models were optimized using the Grey Wolf Optimization (GWO) algorithm. The ensemble models, denoted as ADA-DT, ADA-MLP, and ADA-SVM were assessed based on key performance metrics, including R2, MAE, RMSE, and MAPE. Results demonstrated the efficacy of the ensemble models in capturing complex relationships within the dataset. ADA-DT obtained an exceptional R2 score of 0.99031, highlighting its outstanding predictive accuracy. Similarly, ADA-MLP and ADA-SVM showed great accuracy, achieving R2 of 0.88272 and 0.96842, respectively. In this study, we uncover valuable insights into the application of ensemble methods and hybridized optimization methods for accurate and robust regression modeling in chemical concentration prediction scenarios applicable for petroleum engineering.

OBBStacking: An Ensemble Method for Remote Sensing Object Detection

Ensemble methods are a reliable way to combine several models to achieve superior performance. However, research on the application of ensemble methods in the remote sensing object detection scenario is mostly overlooked. Two problems arise. First, one unique characteristic of remote sensing object detection is the Oriented Bounding Boxes (OBB) of the objects and the fusion of multiple OBBs requires further research attention. Second, the widely used deep learning object detectors provide a score for each detected object as an indicator of confidence, but how to use these indicators effectively in an ensemble method remains a problem. Trying to address these problems, this paper proposes OBBStacking, an ensemble method that is compatible with OBBs and combines the detection results in a learned fashion. This ensemble method helps take 1st place in the Challenge Track <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fine-grained Object Recognition in High-Resolution Optical Images</i> , which was featured in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2021 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation</i> . The experiments on DOTA dataset and FAIR1M dataset demonstrate the improved performance of OBBStacking and the features of OBBStacking are analyzed. Code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Haoning724/obbstacking</uri>

A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry

Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that the random forest with bootstrap class weighting technique has given the most impressive performance for bit type selection with testing accuracy ranges from 92% to 99%, and G-mean (0.84–0.97) in critical to normal experimental scenarios. This study provides an approach to automate the drill bit selection process over any field, which will minimize human error, time, and drilling cost.

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

AbstractFeature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.

An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches

The ensemble approach such as boosting is based on heuristic system to develop prediction paradigms. The ensemble learning techniques are typically more accurate than individual classifiers to generate predictions. For these grounds, primarily in this study several ensemble techniques have been discussed to get a comprehensive knowledge of key methods. Among various ensemble approaches, researchers have practiced boosting mechanism to predict the performance of students. As application of ensemble methods is contemplated to be significant phenomenon in classification and prediction procedures, therefore the researchers exploited boosting technique to develop an accurate prediction pedagogical model, in view of the pronounced nature and novelty of the proposed method in educational data mining. The base classifiers including random tree, j48, knn and naïve bayes have been evaluated on 10- fold cross validation system. Moreover, filtering procedures such as oversampling (SMOTE) and under-sampling (Spread subsampling), have been exploited to further inspect any significant change in results among meta and base classifiers. Both ensemble and filtering approaches have demonstrated substantial improvement in predicting the performance of students than the application of conventional classifiers. Furthermore, based on the improvement in results two novel prediction models have been propounded after conducting performance analysis on each approach.

Using ensemble classification methods in lung cancer disease.

This paper presents an overview of the use of ensemble classification methods in the lung cancer disease. An analysis is carried out according to seven aspects: publication trends, channels and venues; medical tasks tackled; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. The application of ensemble methods in lung cancer disease started in 2003. The diagnosis task was the most tackled one by researchers. Furthermore, the homogeneous ensembles were the most frequent in the literature, and decision tree techniques were the most adopted ones for constructing ensembles. Several datasets related to the lung cancer disease were used to build and assess the ensemble methods. The most used tool was Weka. To conclude, some recommendations for future research are: tackle the medical tasks not investigated in the literature by means of ensemble methods; investigate other classification methods; propose other heterogeneous ensemble methods; and use other combination rules.

Application of ensemble methods for classification of water quality

Groundwater pollution in Shoosh Aquifer located in Khuzestan Province, Iran, was considered, using an eight years time period data set collected from 30 sampling wells. Cluster analysis rendered a dendrogram where 30 sampling wells were grouped into three statistically significant clusters. The classification methods, k-nearest neighbour and classification tree, were utilised to classify sampling stations, with respect to the level of pollution. The optimum tree depth and number of neighbours were determined by 4-fold misclassification error which both had an error of 0.167. An ensemble was created using these base classifiers. In addition, considering the small sample size of our data in this study, random subspace as a feature selection method was amalgamated with k-nearest neighbour ensemble. The misclassification errors of classification tree and k-nearest neighbour ensembles were 0.13 and 0.10, respectively. The results of this study confirmed the high accuracy of ensemble methods for data classification.

Ensemble methods for advanced skier days prediction

The tourism industry has long utilized statistical and time series analysis, as well as machine learning techniques to forecast leisure activity demand. However, there has been limited research and application of ensemble methods with respect to leisure demand prediction. The research presented in this paper appears to be the first to compare the predictive power of ensemble models developed from multiple linear regression (MLR), classification and regression trees (CART) and artificial neural networks (ANN), utilizing local, regional, and national data to model skier days. This research also concentrates on skier days prediction at a micro as opposed to a macro level where most of the tourism applications of machine learning techniques have occurred. While the ANN model accuracy improvements over the MLR and CART models were expected, the significant accuracy improvements attained by the ensemble models are notable. This research extends and generalizes previous ensemble methods research by developing new models for skier days prediction using data from a ski resort in the state of Utah, United States.