Ensemble Classification Approach Research Articles

An Ensemble of Long Short-Term Memory Networks with an Attention Mechanism for Upper Limb Electromyography Signal Classification

Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold.

An Ensemble Machine Learning Approach for Classifying Job Positions

Machine learning is one of the promising research areas in computer science, with numerous applications in automated detection of meaningful data patterns. Several data-centric studies were conducted on evaluating competencies, detecting similar jobs and predicting salaries of various job positions. However, the hazy distinction between closely related job positions requires powerful predictive algorithms. The present study proposed an ensemble approach for accurate classification of various job positions. Accordingly, different machine learning algorithms were applied on 955 instances obtained from Glassdoor using web scraping. Furthermore, the present study classify various job positions based on average salary and other correlated explanatory variables that cover many aspects of job activities on the internet. The study result revealed the superior performance of heterogeneous ensembles in terms of precision and accuracy. The proposed data-centric approach produce strong models for researchers, recruiters, and candidates to assigned job positions and its competencies.

An ensemble-based faults detection and diagnosis approach for determining faults severities at whole-building level

Data-driven fault detection and diagnosis (FDD) approaches rely on operational data even when in-depth knowledge of the studied system is lacking. Simulated data is affordable and can cover numerous fault types and severities. Developing FDD models with a single fault severity has limitations on the model's generalisability owing to evolution of faults with severities. Thus, machine learning models must be individually trained on diverse scenarios. Current studies on building FDD are often at system levels, thereby leaving significant knowledge gaps when there is interest in energy performance of an entire building. This research presents a data-driven FDD approach for classifying several building faults using different ensemble multi-class classification approaches. Additionally, the impact of noise on training datasets is investigated. The XGBoost classifier achieved the highest classification accuracy for the considered faults during validation and testing stages, with low sensitivity to noise, thereby demonstrating a promising potential for wider scale deployment.

Retracted: Ensemble Classification Approach for Sarcasm Detection.

[This retracts the article DOI: 10.1155/2021/9731519.].

Ensemble Classifier for Stroke Prediction with Recurshive Feature Elimination

This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). By iteratively selecting and excluding features, RFE enhances the model's predictive capacity while minimizing overfitting. The ensemble classifier, formed by combining diverse base classifiers, capitalizes on their complementary strengths to enhance overall predictive performance. Leveraging a comprehensive dataset, the proposed approach demonstrates superior stroke prediction accuracy compared to individual classifiers, underscoring its potential as an effective tool for early stroke risk assessment.

Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis.

Breast cancer is one of the most common chronic diseases and the second cause of death among women, where its timely diagnosis plays an important role in survival and treatment. Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. In recent years, the development of these systems with data mining techniques and machine learning approaches has attracted the attention of researchers. This study presents a new hybrid approach using data mining techniques including feature selection and classification. Feature selection is configured using a method based on integrated filter-evolutionary search, where this method includes an evolutionary algorithm and information gain. The proposed feature selection method can provide the most suitable features by reducing dimensions for breast cancer classification. Meanwhile, we introduce an ensemble classification approach based on neural networks whose parameters are adjusted by an evolutionary algorithm. The effectiveness of the proposed method has been evaluated by several real datasets from the UCI machine learning repository. The results of simulations in terms of various metrics such as accuracy, precision and recall show that the proposed method is 12% better than the best existing methods on average. The evaluation of the proposed method confirms its effectiveness for breast cancer diagnosis as an intelligent medical assistant.

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models.

Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging

Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.

A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks

Internet of Things (IoT) enabled networks are highly vulnerable to cyber threats due to insecure wireless communication, resource constraint architecture, different types of IoT devices, and a high volume of sensor data being transported across the network. Therefore, IoT-compatible cybersecurity solutions are required. An intrusion detection system is one of the most common solutions for detecting cyber threats in IoT-enabled networks. However, most of the existing solutions for cyber threat detection suffer from many issues like poor accuracy, high learning complexity, low scalability, and high false positive rate (FPR). We propose a metaheuristic-based intelligent and novel framework for cyber threat detection using ensemble feature selection and classification approaches to overcome these issues. First, a metaheuristic-based ensemble feature selection framework is designed using Binary Gravitational Search Algorithm (BGSA) and Binary Grey Wolf Optimization (BGWO) to get an optimized set of features to avoid the curse of dimensionality for efficient learning. Next, Decision Tree and ensemble learning-based classification techniques such as AdaBoost and Random Forest (RF) are employed separately to detect and classify cyber threats. The UNSW-NB15 dataset assesses the effectiveness of the proposed framework, and its performance is evaluated against recent state-of-the-art frameworks. Based on the result analysis, it is found that the RF outperforms existing modern cyber threats detection methods due to the optimized feature subset (4 features out of 42), maximum accuracy (99.41%), maximum detection rate (99.09%), and maximum F1-score (99.33%) with the lowest FPR (0.03%).

Heart ID: Biometric Identification Using Wearable MIMO RF Heart Sensors

Biometric identification (ID) has become increasingly prevalent in the digital era. Static biometric methods, such as fingerprint and facial recognition are widely accepted, yet generally vulnerable to targeted presentation attacks. Current development has expanded to dynamic biometrics, such as gait and electrocardiogram, that enable continuous authentication and are significantly more resistant to presentation attacks. However, dynamic biometrics often involve cumbersome acquisition which restricts their widespread use. Here, we introduce Heart ID, a novel dynamic biometric system that uses near-field coherent sensing (NCS) with a multiple-in multiple-out (MIMO) radio-frequency (RF) antenna setup to non-invasively acquire detailed recordings of internal cardiac dielectric boundary motion over clothing. NCS couples localized energy to the heart to derive interpersonal structural differences, while MIMO significantly increases the biometric entropy compared to single-point observation. We performed a human study of 20 subjects as well as 2 longitudinal evaluations, and employed an unsupervised feature extraction method to explore the ID performance of this new biometric. We found an ensemble classification approach using features derived from unsupervised learning can achieve accuracy exceeding 99% at a 40-second epoch.

A new ensemble classification approach based on Rotation Forest and LightGBM

A new ensemble classification approach based on Rotation Forest and LightGBM

Active Weighted Aging Ensemble for drifted data stream classification

PurposeOne of the significant problems in data stream classification is the concept drift phenomenon, which consists of the change in probabilistic characteristics of the classification task. Such changes in posterior probability destabilize the classification model performance, seriously degrading its quality. It is necessary to design appropriate strategies to counteract this phenomenon, allowing the classifier to adapt to the changing probabilistic characteristics. It is tough to propose such an approach with limited access to data labels. A human bias of high quality is usually costly, so to minimize the expenses related to this process, it is also necessary to propose learning strategies based on semi-supervised learning. Such strategies employ active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. MethodsThis paper proposes Active Weighted Aging Ensemble algorithm, a novel chunk-based method for non-stationary data stream classification. It employs a classifier ensemble approach and utilizes the changing ensemble lineup to react to concept drift appropriately. It also proposed a new active learning method, considering a limited budget that may be applied to any data stream classifier. ResultsAWAE has been evaluated through computer experiments using real and synthetic data streams, confirming the proposed algorithm's high quality over state-of-the-art methods. ConclusionThe research conducted on benchmark data streams confirmed the effectiveness of the proposed solution and highlighted its strengths in comparison with state-of-the-art methods. The estimated computational complexity is acceptable and comparable to the benchmark algorithms.

Forecasting of Poverty using the Ensemble Learning Classification Methods

Poverty is a social-cultural problem that can be categorized into monetary approach, capability approach, social exclusion, and participatory poverty assessment. However, the existing measurement methods are complex, costly, and time-consuming. This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. Ensemble Learning has improved the classification of poverty and obtained better predictive performance. The results of the algorithms showed the poverty trend, which helped to determine the poverty classification. Hence, this method will help the government to act and produce a specific plan to reduce the poverty rate. It is a strategic move to reduce global poverty, parallel to Goal 1 of Sustainable Development Goal (SDG): No Poverty

Predicting rockburst damage scale in seismically active mines using a classifier ensemble approach

Rockburst still remains one of the most important sources of hazards in underground mine industry. This phenomenon results in many accidents and casualties in many underground mine projects around the world. Despite the scientific achievements in rock mechanics and engineering, the accurate prediction of rockburst damage potential is still challenging due to the complexity of seismic event occurrence. Hence, this paper aims to develop a reliable classifier ensemble to rockburst intensity in underground mine excavations subjected to seismicity. High quality rockburst database consisting of 254 case histories was used for the study. The classifier ensemble was developed through aggregation of several commonly used machine learning classifiers using the weighted voting. The performance of the classifier ensemble was evaluated using several indicators, namely: accuracy, recall, precision, and F1-score. The overall results indicate that the proposed classifier ensemble achieved good performance metrics and outperformed some existing empirical methods. It is concluded that the classifier ensemble could assist engineers to properly assess rockburst damage and contribute to selecting adequate ground control measures.

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.

Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification

Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family.

TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST

The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.

Ensemble voting‐based fault classification and location identification for a distribution system with microgrids using smart meter measurements

AbstractThis study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)‐based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event‐driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k‐nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE‐123 bus system modified with added PV‐based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real‐time simulation‐based testbed using a state‐of‐the‐art digital real‐time simulator, industry standard DNP3 communication protocol and a cpu‐based control centre running the FL algorithm.

Intelligent Intrusion Detection Scheme for Smart Power-Grid Using Optimized Ensemble Learning on Selected Features

The smart grid has gained a reputation as the advanced paradigm of the power grid. It is a complicated cyber-physical system that combines information and communication technology (ICT) with a traditional grid that can remotely control operations. It provides the medium for exchanging real-time data between the company and users through the advanced metering infrastructure (AMI) and smart meters. However, smart grids have many security and privacy concerns, such as intruding sensitive data, firmware hijacking, and modifying data due to the high reliance on ICT. To protect the power-grid system from these counteracts and for reliable and efficient power distribution, early and accurate identification of these issues needs to be addressed. The intrusion detection in a smart grid system plays an essential role in providing a secure service and transmitting the high priority alert message to the system admin about the detection of adversary attacks. This paper proposes an intelligent intrusion detection scheme to accurately classify various attacks on smart power grid systems. The proposed scheme used the binary grey wolf optimization-based feature selection. It optimized the ensemble classification approach to learn the non-linear, overlapping, and complex electrical grid features taken from publicly available Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) dataset. The experimental results using a 10-fold cross-validation setup and selected feature subset for two class and three class problems reveal the proposed method's promising performance. Further, the significantly superior performance compared to the existing benchmark methods justified the robustness of the proposed scheme.