Ensemble Of Classifiers Research Articles

Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential

Background and objectiveBrain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP. MethodsThe initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts. ResultsThe ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % ± 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types. ConclusionsThe results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.

An online ensemble classification algorithm for multi-class imbalanced data stream

An online ensemble classification algorithm for multi-class imbalanced data stream

Optimization assisted ensemble classification for prediction of chronic kidney disease

Optimization assisted ensemble classification for prediction of chronic kidney disease

Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques.

Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease. The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context. A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc. When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields. Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.

Nelder-Mead Optimized Weighted Voting Ensemble Learning for Network Intrusion Detection

The rise in internet usage and data transfer rates has led to numerous anomalies. Hence, anomaly-based intrusion detection systems (IDS) are essential in cybersecurity because of their ability to identify unknown cyber-attacks, especially zero-day attacks that signature-based IDS cannot detect. This study proposes an ensemble classification for intrusion detection using a weighted soft voting system with KNN, XGBoost, and Random Forest base models. The base model weights are optimized using the Nelder-Mead simplex method to improve the overall ensemble performance. We propose a robust intrusion detection framework that uses soft-voting classifier-level weights optimized using the Nelder-Mead algorithm and feature selection. We evaluated the system's performance using the KDD99 and UNSW-NB15 datasets, which demonstrated that the proposed approach exceeded other existing methods in respect of accuracy and provided comparable results with fewer features. The proposed system and its hyperparameter optimization technique were compared with other cyber threat detection and mitigation systems to determine their relative effectiveness and efficiency.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images

ABSTRACT Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system’s efficiency of detecting cancer and help the patient to get the appropriate treatment.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data

ABSTRACT The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn’s disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Hybrid optimization assisted deep ensemble classification framework for skin cancer detection

Hybrid optimization assisted deep ensemble classification framework for skin cancer detection

DETECTION AND CLASSIFICATION OF COVID-19 USING GRAY-LEVEL FEATURES AND ENSEMBLE CLASSIFIER

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database . Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.

Manifold neighboring envelope sample generation mechanism for imbalanced ensemble classification

For existing imbalanced ensemble (IE) methods, the sample subsets are constructed from the same dataset, which usually suffer from low quality (diversity and separability) of the subsets, so a manifold neighboring envelope sample generation mechanism (MNESG) and an imbalanced ensemble algorithm based on the mechanism (MNESG-IE) are proposed to solve this problem. First, for the original balanced subsets (OBS), a manifold neighboring sample envelope projection mechanism (MNSEP) is designed to mine the local correlation information between the samples and their nearest neighbors in the subsets. Second, the fuzzy c-means clustering (FCM) is used to further mine the global correlation information among similar samples in the subsets. Third, the sample distribution consistency preservation mechanism (SDCPM) is designed to enhance the consistency of the sample distribution before and after clustering. To better reduce the three accumulated losses above, the three steps are conducted simultaneously, thereby realizing the MNESG, which can transform the OBS into two new types of high quality envelope sample subsets − neighboring envelope sample (NES) subsets and neighboring cluster envelope sample (NCES) subsets. Finally, base classifiers are trained on the NES subsets and NCES subsets, and then fused by a two-dimensional sparse fusion mechanism (2D-SFM). Various representative IE algorithms on over thirty benchmark datasets are considered for verification. The results show that compared with the state-of-the-art IE algorithms, MNESG-IE achieves 17.79%, 17.90%, 23.61%, 18.08% improvement in terms of ACC, AUC, F-M and G-M, respectively. The major originality of the paper is: (a) proposing the MNSEP to mine the local correlation information for improving the quality of the subsets; (b) proposing the MNESG to generate high quality subsets by mining local and global correlation information simultaneously; and (c)forming an IE algorithm to better solve the imbalanced classification problem.

Insights into ensemble learning classification tools allied with near infrared spectroscopy for diesel quality management

The effective management of diesel quality is not only the fundamental guarantee for the normal operation of diesel engine, but also of realistic significance to protect the environment and personal safety. This paper presented an effective strategy for the rapid classification and determination of the compliance of diesel by using near infrared (NIR) spectroscopy, coupled with Tree-based feature selection (Tree) and popular ensemble learning frameworks. To verify the extrapolation and adaptability of ensemble learning, the light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) which serve as baselines and their combination models with Tree method were developed to establish the models for the classification of diesel brands (case 1: −10#, −20#, −35#, −50# and inferior diesel) and alcohols diesel types (case 2: methanol diesel, ethanol diesel and pure diesel). Satisfactory results showed that all models have achieved acceptable classification results in the above two cases, especially the accuracy of Tree-LightGBM can reach more than 97 % without being affected by data imbalance and spectral overlap. The proposed approach that uses the NIR spectroscopy for diesel brands and types classification can be highly recommended as a practical, convenient, environment friendly and reliable solution for the routine monitoring of diesel market.

Crop Recommendation System Using Machine Learning: A Comparative Study

Agriculture, as a fundamental aspect of human existence, faces challenges in crop selection, impacting resource allocation and productivity. This project addresses these challenges by proposing a stable system employing a soft voting classifier ensemble method. The ensemble comprises Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest classifiers, offering personalized crop recommendations. Feasibility analysis encompasses technical, operational, economic, and scheduling aspects, ensuring practicality and efficacy. Development follows an incremental model, emphasizing continuous enhancement through feedback. Results indicate accuracies for individual classifiers (’Decision Tree’: 98.38%, ’Random Forest’: 98.90%, ’Naive Bayes’: 98.14%, ’SVM’: 98.50%), with an ensemble accuracy of 98.99%. Cross-validation confirms robustness. Evaluation metrics such as recall, precision, and F1 score demonstrate that the soft voting ensemble outperforms individual classifiers, highlighting its effectiveness in optimizing crop selection processes in agriculture and facilitating improved resource management and productivity.

Enhancing trustworthiness among iot network nodes with ensemble deep learning-based cyber attack detection

A lot of machine learning methods and expert systems are used in network intrusion detection automation. When different industrial control systems merge with the Internet of Things (IoT) environment, they become vulnerable to cyber-attacks in critical infrastructure situations requiring communication technologies. Conventional machine learning techniques used for network anomaly detection are ineffective due to the substantial amount of network traffic within important Cyber-Physical Systems (CPSs). In this manuscript, Cyberattack Identification Through Ensemble Deep Learning in an IoT environment is proposed. Initially, the input network traffic data are taken from the IoT-23 dataset. Then the network traffic data are preprocessed using Z-Score normalization to reduce any irrelevant or erroneous data from the input dataset. Then the relevant features are selected using the Gorilla Troops Optimization (GTO) algorithm. Afterwards, the selected features are fed into the ensemble classification model based on Random Space (RS), Random Tree (RT), Extreme Gradient Boosting (XGBoost), and Graph Convolutional Neural Network (GCNN). Among the several ensembling techniques, GCNN can achieve better performance. Python is used to accomplish the suggested technique. The performance of the proposed GCNN method provides 12.09%, 4.34%, and 3.21% higher accuracy than the other models like RS, RT and XGBoost respectively.

Building Materials Classification Model Based on Text Data Enhancement and Semantic Feature Extraction

In order to accurately extract and match carbon emission factors from the Chinese textual building materials list and construct a precise carbon emission factor database, it is crucial to accurately classify the textual building materials. In this study, a novel classification model based on text data enhancement and semantic feature extraction is proposed and applied for building materials classification. Firstly, the explanatory information on the building materials is collected and normalized to construct the original dataset. Then, the Latent Dirichlet Allocation and statistical-language-model-based hybrid ensemble data enhancement methods are explained in detail, and the semantic features closely related to the carbon emission factor are extracted by constructed composite convolutional networks and the transformed word vectors. Finally, the ensemble classification model is designed, constructed, and applied to match the carbon emission factor from the textual building materials. The experimental results show that the proposed model improves the F1Macro score by 4–12% compared to traditional machine learning and deep learning models.

Domain adaptation via gamma, Weibull, and lognormal distributions for fault detection in chemical and energy processes

AbstractThe burgeoning development of supervised machine learning (ML) has led to its widespread applications in chemical and energy processes, such as fault detection. However, in some scenarios, collecting labelled data can be costly, hazardous, or impossible. Moreover, data of the same process can follow varying distributions due to changes in, for example, devices and environment, causing ML models to be ineffective. These challenges pose a domain adaptation task, necessitating the refinement of existing ML models to tackle issues from related applications. This study proposes a domain adaptation approach to address label scarcity and data distribution variation. The method has three stages: data distribution modelling (knowledge discovery), adaptation of target domain samples to source domains (knowledge transformation), and classifier ensemble for fault detection (knowledge fusion). Gamma, Weibull, and lognormal distributions are applied for data modelling and domain adaptation. The effectiveness of the method is validated on synthetic datasets and then applied to identify anomalies in coal mine pressure data and detect faults in the Tennessee Eastman (TE) process.

Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease

Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease

Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

Enhancing Rock Fragmentation in Mining: Leveraging Ensemble Classification Machine Learning Algorithms for Blast Toe Volume Assessment

Enhancing Rock Fragmentation in Mining: Leveraging Ensemble Classification Machine Learning Algorithms for Blast Toe Volume Assessment

Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images

BackgroundThe development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using artificial intelligence. However, only two papers have dealt with complex BI-RADS classifications. PurposeThis study addresses the automatic classification of breast lesions into binary classes (benign vs. malignant) and multiple BI-RADS classes based on a single ultrasonographic image. Achieving this task should reduce the subjectivity of an individual operator’s assessment. Materials and MethodsAutomatic image segmentation methods (PraNet, CaraNet and FCBFormer) adapted to the specific segmentation task were investigated using the U-Net model as a reference. A new classification method was developed using an ensemble of selected segmentation approaches. All experiments were performed on publicly available BUS B, OASBUD, BUSI and private datasets. ResultsFCBFormer achieved the best outcomes for the segmentation task with intersection over union metric values of 0.81, 0.80 and 0.73 and Dice values of 0.89, 0.87 and 0.82, respectively, for the BUS B, BUSI and OASBUD datasets. Through a series of experiments, we determined that adding an extra 30-pixel margin to the segmentation mask counteracts the potential errors introduced by the segmentation algorithm. An assembly of the full image classifier, bounding box classifier and masked image classifier was the most accurate for binary classification and had the best accuracy (ACC; 0.908), F1 (0.846) and area under the receiver operating characteristics curve (AUROC; 0.871) in the BUS B and ACC (0.982), F1 (0.984) and AUROC (0.998) in the UCC BUS datasets, outperforming each classifier used separately. It was also the most effective for BI-RADS classification, with ACC of 0.953, F1 of 0.920 and AUROC of 0.986 in UCC BUS. Hard voting was the most effective method for dichotomous classification. For the multi-class BI-RADS classification, the soft voting approach was employed. ConclusionsThe proposed new classification approach with an ensemble of segmentation and classification approaches proved more accurate than most published results for binary and multi-class BI-RADS classifications.

Explainable feature selection and ensemble classification via feature polarity

Feature selection aims to choose the most relevant features from the dataset that can enhance the performance and efficiency of machine learning models. Although feature selection has been studied for many years, most existing methods focus on accuracy and efficiency while neglecting the interpretability of selected features. Therefore, inspired by the “Yin-Yang” philosophy, we introduce the concept of feature polarity for the first time and divide the features into positive and negative features. For example, by analyzing a patient's symptoms (features), we can obtain two sets of features to explain whether the patient has the flu. Positive features help us determine if the patient has the flu, while negative features can help us rule out the possibility of the flu. We introduce the PN (Positive and Negative) coefficient to measure the polarity of candidate features and develop a novel and explainable feature selection method based on feature polarity. Furthermore, we propose an ensemble classification framework that leverages both positive and negative features for each class to improve classification performance. Extensive experiments demonstrate the effectiveness of the PN coefficient compared to other information measurements. Moreover, our proposed classification framework performs excellently compared to some state-of-the-art feature selection methods.