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MLPNN and ensemble learning algorithm for Transmission Line Fault Classification

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Rahman, Tanbir Hasan, Talab Ahammad, Arif Ahmed, Imtiaz Rakhaine, Nainaiu Sylhet Engn Coll Dept Elect & Elect Engn Sylhet Bangladesh Shahjalal Univ Sci & Technol Dept Elect & Elect Engn Sylhet Bangladesh

Recently, Bangladesh experienced a system loss of 11.11%, leading to significant power cuts, largely due to faults in power transmission lines. This paper proposes the XGBoost machine learning method for classifying electric power transmission line faults. The study compares multiple machine learning approaches, including ensemble methods (decision tree, random forest, XGBoost, CatBoost, and LightGBM) and the multilayer perceptron neural network (MLPNN), under various conditions. The power transmission system is modeled using Simulink and the machine learning algorithms. In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. However, although all of the methods gain significant accuracy, assessing the performance results indicates that the XGBoost model is the most effective for transmission line fault classification among the methods tested, as it showed the best accuracy in the imbalanced and noisy state's classification of faults, contributing to the development of more reliable and efficient fault detection methodologies for power transmission networks.

关键词： CatBoost classification data imbalance ensemble learning algorithm noise transmission line faults XGBoost

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ensemble learning algorithm based on multi-parameters for sleep staging

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第8期57卷 1693-1707页

作者： Wang, Qiangqiang Zhao, Dechun Wang, Yi Hou, Xiaorong Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Med Univ Chongqing Peoples R China

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification *** results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging.

关键词： EEG signal Sleep stage Feature selection ensemble learning algorithm Stacking

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Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm

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INFORMATION FUSION 2017年 35卷 1-10页

作者： Li, Jing Song, Ningfang Yang, Gongliu Li, Ming Cai, Qingzhong Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China

Recently, methods based on Artificial Intelligence (Al) have been widely used to improve positioning accuracy for land vehicle navigation by integrating the Global Positioning System (GPS) with the Strapdown Inertial Navigation System (SINS). In this paper, we propose the ensemble learning algorithm instead of traditional single neural network to overcome the limitations of complex and dynamic data cased by vehicle irregular movement. The ensemble learning algorithm (LSBoost or Bagging), similar to the neural network, can build the SINS/GPS position model based on current and some past samples of SINS velocity, attitude and IMU output information. The performance of the proposed algorithm has been experimentally verified using GPS and SINS data of different trajectories collected in some land vehicle navigation tests. The comparison results between the proposed model and traditional algorithms indicate that the proposed algorithm can improve the positioning accuracy for cases of SINS and specific GPS outages. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sins/gps integration Gps outages Neural network ensemble learning algorithm

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PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2023年 21卷 3604-3614页

作者： Yang, Tao Yan, Qiyu Long, Rongzhuo Liu, Zhixian Wang, Xiaosheng China Pharmaceut Univ Sch Basic Med & Clin Pharm Biomed Informat Res Lab Nanjing 211198 Peoples R China China Pharmaceut Univ Canc Genom Res Ctr Sch Basic Med & Clin Pharm Nanjing 211198 Peoples R China China Pharmaceut Univ Big Data Res Inst Nanjing 211198 Peoples R China Nanjing Med Univ Jiangsu Canc Hosp Jiangsu Inst Canc Res Affiliated Canc Hosp Nanjing Jiangsu Peoples R China

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at https://***/WangX-Lab/PreCanCell.

关键词： ensemble learning algorithm Single -cell transcriptomes Cancer and non -cancer cells Tumor marker genes Non -tumor marker genes

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Wrapper Feature Subset Selection for Dimension Reduction Based on ensemble learning algorithm 3rd

Wrapper Feature Subset Selection for Dimension Reduction Bas...

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3rd Information Systems International Conference

作者： Panthong, Rattanawadee Srivihok, Anongnart Kasetsart Univ Dept Comp Sci Bangkok 10900 Thailand

Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on the use of wrapper feature selection. This study use methods of sequential forward selection (SFS), sequential backward selection (SBS) and optimize selection (evolutionary) based on ensemble algorithms namely Bagging and AdaBoost by subset evaluations which are performed using two classifiers;Decision Tree and Naive Bayes. Thirteen datasets containing different numbers of attributes and dimensions are obtained from the UCI Machine learning Repository. This study shows that the search technique using SFS based on the bagging algorithm using Decision Tree obtained better results in average accuracy (89.60%) than other methods. The benefits of the feature subset selection are an increased accuracy rate and a reduced run-time when searching multimedia data consisting of a large number of multidimensional datasets. (C) 2015 Published by Elsevier B.V.

关键词： Feature selection ensemble learning algorithm dimension reduction classification data mining

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HELA: A novel hybrid ensemble learning algorithm for predicting academic performance of students

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EDUCATION AND INFORMATION TECHNOLOGIES 2022年第4期27卷 4521-4552页

作者： Keser, Sinem Bozkurt Aghalarova, Sevda Eskisehir Osmangazi Univ Dept Comp Engn Eskisehir Turkey

Education plays a major role in the development of the consciousness of the whole society. Education has been improved by analyzing educational data related to student academic performance. By using data mining techniques and algorithms on data from the educational environment, students' performances can be predicted. In this study, a novel Hybrid ensemble learning algorithm (HELA) is proposed to predict the academic performance of students. The prediction results obtained from base classifiers namely Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and different combinations of these algorithms are given as input to the Super Learner algorithm. Hyper-parameters of base classifiers are optimized with a Random Search algorithm. Students' performances in Math and Portuguese classes are predicted by the proposed algorithm. In the experimental results, 96.6% and 91.2% accuracy values are obtained for the Mathematics course, and the Portuguese course, respectively. This paper is the first study, to our knowledge, to integrate the boosting and stacking-based ensemble learning algorithm for the prediction of students' academic performance that gives better predictive results with high efficiency.

关键词： Educational data mining ensemble learning algorithm Prediction of academic performance Classification

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An online portfolio strategy based on trend promote price tracing ensemble learning algorithm

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷

作者： Dai, Hong-Liang Liang, Chu-Xin Dai, Hong-Ming Huang, Cui-Yin Adnan, Rana Muhammad Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Guangdong Polytech Sci & Trade Sch Informat & Automat Guangzhou 510430 Peoples R China

How to carry out an investment portfolio efficiently and reasonably has become a hot issue. This study mainly addresses the problem of the instability of forecasting stock price investment and the difficulty in determining investment proportion by proposing the trend peak price tracing (TPPT). First of all, because of the influence of stock price anomaly, TPPT strategy sets adjustable historical window width. It uses slope value to judge prediction direction to track price change, which uses exponential moving average and peak equal weight slope value three-state price prediction method. Secondly, the accumulated wealth target is refined, and the fast error Back Propagation based on gradient projection algorithm (BP) is added. The algorithm solves investment proportion and feedbacks the increasing ability of assets to the investment proportion in order to maximize the accumulated wealth. Finally, comparison of eight empirical strategies in five typical data and statistical tests show that TPPT strategy has great advantages in balancing risk and return, and it is a robust and effective online portfolio strategy. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Online portfolio investment Price anomaly Three-state price Gradient projection ensemble learning algorithm Investment ratio

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Dam Deformation Prediction Considering the Seasonal Fluctuations Using ensemble learning algorithm

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BUILDINGS 2024年第7期14卷

作者： Liu, Mingkai Feng, Yanming Yang, Shanshan Su, Huaizhi Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China PowerChina Kunming Engn Co Ltd Kunming 650000 Peoples R China Yunnan Key Lab Water Conservancy & Hydropower Engn Kunming 650051 Peoples R China Hohai Univ Cooperat Innovat Ctr Water Safety & Hydro Sci Nanjing 210024 Peoples R China

Dam deformation is the most visual and relevant monitoring quantity that reflects the operational condition of a concrete dam. The seasonal variations in the external environment can induce seasonal fluctuations in the deformation of concrete dams. Hence, preprocessing the deformation monitoring series to identify seasonal fluctuations within the series can effectively enhance the accuracy of the predictive model. Firstly, the dam deformation time series are decomposed into the seasonal and non-seasonal components based on the seasonal decomposition technique. The advanced ensemble learning algorithm (Extreme Gradient Boosting model) is used to forecast the seasonal and non-seasonal components independently, as well as employing the Tree-structured Parzen Estimator (TPE) optimization algorithm to tune the model parameters, ensuring the optimal performance of the prediction model. The results of the case study indicate that the predictive performance of the proposed model is intuitively superior to the benchmark models, demonstrated by a higher fitting accuracy and smaller prediction residuals. In the comparison of the objective evaluation metrics RMSE, MAE, and R2, the proposed model outperforms the benchmark models. Additionally, using feature importance measures, it is found that in predicting the seasonal component, the importance of the temperature component increases, while the importance of the water pressure component decreases compared to the prediction of the non-seasonal component. The proposed model, with its elevated predictive accuracy and interpretability, enhances the practicality of the model, offering an effective approach for predicting concrete dam deformation.

关键词： dam deformation seasonal fluctuations ensemble learning algorithm tree-structured parzen estimator feature importance metrics

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Wrapper Feature Subset Selection for Dimension Reduction Based on ensemble learning algorithm

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Procedia Computer Science 2015年 72卷 162-169页

作者： Rattanawadee Panthong Anongnart Srivihok Computer Science Department Kasetsart University Bangkok and 10900 Thailand

Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on the use of wrapper feature selection. This study use methods of sequential forward selection (SFS), sequential backward selection (SBS) and optimize selection (evolutionary) based on ensemble algorithms namely Bagging and AdaBoost by subset evaluations which are performed using two classifiers; Decision Tree and Naïve Bayes. Thirteen datasets containing different numbers of attributes and dimensions are obtained from the UCI Machine learning Repository. This study shows that the search technique using SFS based on the bagging algorithm using Decision Tree obtained better results in average accuracy (89.60%) than other methods. The benefits of the feature subset selection are an increased accuracy rate and a reduced run-time when searching multimedia data consisting of a large number of multidimensional datasets.

关键词： Feature selection ensemble learning algorithm dimension reduction classification data mining

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Fall detection system for elderly people using IoT and ensemble machine learning algorithm

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PERSONAL AND UBIQUITOUS COMPUTING 2019年第5-6期23卷 801-817页

作者： Yacchirema, Diana Suarez de Puga, Jara Palau, Carlos Esteve, Manuel Escuela Politec Nacl Ladron Guevara E11-25317-01-2759 Quito Ecuador Univ Politecn Valencia Camino Vera S-N E-46022 Valencia Spain

Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.

关键词： Fall detection Internet of Things 6LowPAN IoT gateway ensemble learning algorithm Random Forest Accelerometer sensor Elderly people

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