Ensemble Techniques for Credibility Estimation of GAME Models

2005 ◽  
pp. 127-132
Author(s):  
Pavel Kordík ◽  
Miroslav Šnorek
Keyword(s):  
Ensemble Techniques

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

scholarly journals Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

2021 ◽  
Vol 11 (9) ◽  
pp. 4281
Author(s):  
Dimitrios Amanatidis ◽  
Ifigeneia Mylona ◽  
Irene (Eirini) Kamenidou ◽  
Spyridon Mamalis ◽  
Aikaterini Stavrianea
Keyword(s):  
English Language ◽  
Visual Features ◽  
Effective Vaccine ◽  
Vaccine Research ◽  
Video Sharing ◽  
Ensemble Techniques ◽  
Significant Events ◽  
Vaccine Treatment ◽  
Personal Experiences ◽  
Negative Sentiment

Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions.

An empirical study for method level refactoring prediction by ensemble technique and SMOTE to improve its efficiency

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Empirical Study ◽  
Sampling Techniques ◽  
Ensemble Technique ◽  
Ensemble Techniques ◽  
Functional Attributes ◽  
Code Refactoring ◽  
Optimized Model ◽  
The Mean ◽  
Box Plot ◽  
Prediction Efficiency

Code refactoring is the modification of structure with out altering its functionality. The refactoring task is critical for enhancing the qualities for non-functional attributes, such as efficiency, understandability, reusability, and flexibility. Our research aims to build an optimized model for refactoring prediction at the method level with 7 ensemble techniques and verities of SMOTE techniques. This research has considered 5 open source java projects to investigate the accuracy of our anticipated model, which forecasts refactoring applicants by the use of ensemble techniques (BAG-KNN, BAG-DT, BAG-LOGR, ADABST, EXTC, RANF, GRDBST). Data imbalance issues are handled using 3 sampling techniques (SMOTE, BLSMOTE, SVSMOTE) to improve refactoring prediction efficiency and also focused all features and significant features. The mean accuracy of the classifiers like BAG- DT is 99.53% ,RANF is 99.55%, and EXTC is 99.59. The mean accuracy of the BLSMOTE is 97.21%. The performance of classifiers and sampling techniques are shown in terms of the box-plot diagram.

scholarly journals COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

2021 ◽  
Vol 11 (23) ◽  
pp. 11423
Author(s):  
Chandrakanta Mahanty ◽  
Raghvendra Kumar ◽  
Panagiotis G. Asteris ◽  
Amir H. Gandomi
Keyword(s):  
Transfer Learning ◽  
Ensemble Methods ◽  
Cost Effective ◽  
Majority Voting ◽  
Learning Models ◽  
Medical Practitioners ◽  
Healthcare Facilities ◽  
Ensemble Techniques ◽  
Effective Manner ◽  
Detection Strategies

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

scholarly journals EVALUATING EFFECTIVENESS OF ENSEMBLE CLASSIFIERS WHEN DETECTING FUZZERS ATTACKS ON THE UNSW-NB15 DATASET

2020 ◽  
Vol 36 (2) ◽  
pp. 173-185
Author(s):  
Hoang Ngoc Thanh ◽  
Tran Van Lang
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Cyber Security ◽  
Experimental Results ◽  
Ensemble Classifiers ◽  
Research Results ◽  
Ensemble Techniques ◽  
F Measure ◽  
Classification Quality

The UNSW-NB15 dataset was created by the Australian Cyber Security Centre in 2015 by using the IXIA tool to extract normal behaviors and modern attacks, it includes normal data and 9 types of attacks with 49 features. Previous research results show that the detection of Fuzzers attacks in this dataset gives the lowest classification quality. This paper analyzes and evaluates the performance of using known ensemble techniques such as Bagging, AdaBoost, Stacking, Decorate, Random Forest and Voting to detect FUZZERS attacks on UNSW-NB15 dataset to create models. The experimental results show that the AdaBoost technique with the component classifiers using decision tree for the best classification quality with F-Measure is 96.76% compared to 94.16%, which is the best result obtained by using single classifiers and 96.36% by using the Random Forest technique.

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