Ensemble Feature Extraction Research Articles

An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data.

Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.

Ensemble Feature Extraction with Classification Integrated with Mask RCNN Architecture in Breast Cancer Detection Based on Deep Learning Techniques

Ensemble Feature Extraction with Classification Integrated with Mask RCNN Architecture in Breast Cancer Detection Based on Deep Learning Techniques

A Novel Paddy Leaf Disease Detection Framework using Optimal Leaf Disease Features in Adaptive Deep Temporal Context Network

Since paddy has become the staple food for all human beings, crop productivity is highly demanded. Nowadays, the agriculture industry faces the leaf disease issue as the insect or pests affects the plant leaves to hinder further growth. Owing to this, the productivity gets affected that makes the farmers have economic loss. In earlier time, several methods have been explored to detect the disease significantly. However, such methods become more time consuming, structure complexity and other issues. To alleviate such complex, a new paddy leaf disease detection model is proposed using adaptive methodology. Initially, images related with paddy leaf are gathered from standard resources and offered as the input to segmentation region. Here, segmentation is performed by Fuzzy C-Means (FCM) to get the abnormal regions. Then, the segmented images are fed to ensemble feature extraction region to attain different features like deep, textural, morphological, and color features. Further, the acquired ensemble features are provided to concatenation phase to obtain the concatenate features and the optimal features are selected by the Fire Hawk Optimizer (FHO). Finally, the optimal features are subjected to paddy leaf detection phase, where leaf disease will be detected by Adaptive Deep Temporal Context Network (ADTCN), where the parameters are tuned by the FHO. Hence, the developed model secures efficient leaf disease detection rate than the classical techniques in the experiential analysis.

Evaluation of Wavelet Transform Based Feature Extraction Techniques for Detection and Classification of Faults on Transmission Lines Using WAMS Data

The smart grid is an intelligent power system network that should be reliable and resilient for sustainable operation. Wide-Area Measurement Systems (WAMS) are deployed in the power grid to provide real-time situational awareness to the power grid operators. An excellent strategy for exploiting the WAMS data effectively is to extract relevant insights from the increasing volume of data collected. Feature extraction techniques are pivotal in developing data-driven models for power systems. This paper proposes an ensemble feature extraction method for developing intelligent data-driven models for transmission line fault detection and classification. A comparative efficacy analysis of the proposed ensemble feature extraction method is carried out with state-of-the-art feature extraction methods. The models developed and evaluated with the feature data derived with the proposed method give an accuracy of 100% for fault detection and 99.78% for fault classification. This method also has the advantage of significantly reducing training and testing time. Features are extracted from the WAMS data collected by simulating an IEEE39 bus test system in the PowerWorld simulator.

Sputum smears quality inspection using an ensemble feature extraction approach.

The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%.

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and diagnosis of these diseases can greatly reduce complications and improve outcomes for high-risk individuals. One method for detecting CVDs is through the use of electrocardiogram (ECG) monitoring systems, which use various technologies such as the Internet of Things (IoT), mobile applications, wireless sensor networks (WSN), and wearable devices to acquire and analyze ECG data for early diagnosis. However, despite the prevalence of these systems in the literature, there is a need for further optimization and improvement of their classification accuracy. In an effort to address this challenge, a novel heterogeneous unsupervised learning model for real-time ECG classification was proposed. The main goal of this work was to reduce the error rate and improve the classification accuracy of the system. This study presents a framework for the classification of multi-class abnormalities in electrocardiograms (ECGs) using an ensemble feature extraction technique and unsupervised learning. The framework utilizes a real-time electrocardiogram-cardiotocography (ECG-CTG) system to extract features from the ECG signal, and then employs an ensemble of feature extraction techniques to enhance the discrimination of the extracted features. The extracted features are then used in an unsupervised learning-based classification algorithm to classify the ECG signals into different classes of abnormalities. The proposed framework is evaluated on a dataset of ECG signals and the results show that it can effectively classify ECG signals with high accuracy and low computational complexity.

Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

The classification of Cardiotocography (CTG) signal abnormalities is critical in the identification of fetal anomalies. The non-stationary nature of CTG, as well as the dataset asymmetry, make this classification task complicated. Manual CTG analysis interpretation can easily result in a low diagnosis rate, which is mainly caused by a variety of subjective variables. Hence, in this research a new Ensemble Feature Extraction (EFE) model is implemented to analyze the nonlinear cardiotocographic signals. First, the collected signal artifacts are removed with interpolation operation, then down sampled to eliminate the complexity during classification. Followed by, a new EFE module is applied to extract the features, here learning model SE-ResNet-50 (Squeeze and Excitation -ResNet-50) is applied to separate the features from the regular and Time-Frequency data transformed (via CWT transform) signal. Wavelet Packet Transform is exploited to extract T-F signals. Then the enhanced feature set dimension is lessened by using Linear Discriminant Analysis (LDA) model. Finally, the features are fused and fed to the XG-booster for classification, and the patient is advised to seek therapy based on the outcome. The proposed method achieved 96.34% accuracy, 96.7% precision, 97.3% recalls, and 95.64% Quality Index (QI) on two-class classifications. This ensemble feature extraction increases the classification results and proves very advantageous to cardiotocographic signal classification. The results of the experiments show how effective the suggested EFE is.

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study’s findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

The changing landscape of power grids with distributed energy sources and power electronic devices has led to increasing relay maloperations. Wide area backup protection is necessary for the resolution of faults and for a reliable power grid. This paper presents detecting and classifying faults in transmission lines for wide-area backup protection using phasor measurement units (PMU) data. The faults are detected and classified using a Weighted Extreme Learning Machine (WELM) algorithm, which considers the variable distribution of data among the different classes using a weighted approach. The PMU signal data used were generated by the simulation of an IEEE 39 bus test system in the PowerWorld/OpenPDC/MATLAB environment. For classification, the input features data were derived using a wavelet transform-based ensemble feature extraction technique, and the WELM classifier was optimized using Particle Swarm Optimization (PSO). The PSO optimized WELM (PSO-WELM) model trained on PMU data detected faults with 100% accuracy and classified them into different types with an accuracy of 99.85%. It is validated that the PSO-WELM outperforms other known classifiers on performance comparison. The model also classified noisy data with a signal-to-noise ratio (SNR) as low as 10 dB and with an accuracy of 97%.

Prediction of diabetic protein markers based on an ensemble method

Introduction: A diabetic protein marker is a type of protein that is closely related to diabetes. This kind of protein plays an important role in the prevention and diagnosis of diabetes. Therefore, it is necessary to identify an effective method for predicting diabetic protein markers. In this study, we propose using ensemble methods to predict diabetic protein markers. Methodological issues: The ensemble method consists of two aspects. First, we combine a feature extraction method to obtain mixed features. Next, we classify the protein using ensemble classifiers. We use three feature extraction methods in the ensemble method, including composition and physicochemical features (abbreviated as 188D), adaptive skip gram features (abbreviated as 400D) and g-gap (abbreviated as 670D). There are six traditional classifiers in this study: decision tree, Naive Bayes, logistic regression, part, k-nearest neighbor, and kernel logistic regression. The ensemble classifiers are random forest and vote. First, we used feature extraction methods and traditional classifiers to classify protein sequences. Then, we compared the combined feature extraction methods with single methods. Next, we compared ensemble classifiers to traditional classifiers. Finally, we used ensemble classifiers and combined feature extraction methods to predict samples. Results: The results indicated that ensemble methods outperform single methods with respect to either ensemble classifiers or combined feature extraction methods. When the classifier is a random forest and the feature extraction method is 588D (combined 188D and 400D), the performance is best among all methods. The second best ensemble feature extraction method is 1285D (combining the three methods) with random forest. The best single feature extraction method is 188D, and the worst one is g-gap. Conclusion: According to the results, the ensemble method, either the combined feature extraction method or the ensemble classifier, was better than the single method. We anticipate that ensemble methods will be a useful tool for identifying diabetic protein markers in a cost-effective manner.

A novel ensemble local graph structure based feature extraction network for EEG signal analysis

Electroencephalogram (EEG) signals have been extensively utilized to identify brain disorders such as epilepsy. In this study, a novel feature extraction network based on local graph structure (LGS) is utilized for EEG signal classification. The aim of this work is to create a framework which utilize ensemble of LGS that uses logically extended LGS, symmetric LGS, vertical LGS, vertical symmetric LGS, zigzag horizontal LGS, zigzag horizontal middle LGS, zigzag vertical LGS and zigzag vertical middle LGS. By using these LGS methods with discrete wavelet transform (DWT), a novel ensemble feature extraction network is formed. In this framework, LGSs are utilized for feature extraction and 2D-DWT is utilized for pooling. In the feature reduction phase, two widely known feature reduction techniques, namely ReliefF and neighborhood component analysis (NCA) are used together. Five different benchmark classifiers are employed to present the strength of the proposed ensemble feature extraction framework. In the experiments, two publicly available EEG datasets have been employed to test the proposed ensemble LGS feature extraction based multilevel EEG signal classification method. The proposed ensemble LGS method achieved 97.20% and 98.67% success rate for these datasets. Six cases were also examined to comprehensively evaluate the used Bonn dataset. Results clearly illustrated the success of the ensemble LGS based EEG classification method.

A Construction of Robust Representations for Small Data Sets Using Broad Learning System

Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article extends the broad learning system (BLS) to a framework for constructing robust representations in low-dimensional and small data sets. First, the BLS changed from a supervised prediction method to an ensemble feature extraction method. Second, feature extraction methods instead of random mapping are used to generate mapped features. Third, deep representations, called enhancement features, are learned from the ensemble mapped features. Fourth, data for generating mapped features and enhancement features can be randomly selected. The ensemble of mapped features and enhancement features can provide robust representations to enhance the performance of downstream tasks. A label-based autoencoder (LA) is embedded in the BLS framework as an example to show the effectiveness of the framework. A random LA (RLA) is presented to generate more different features. The experimental results show that the BLS framework can construct robust representations and significantly promote the performance of machine learning models.

Latent subclass learning-based unsupervised ensemble feature extraction method for hyperspectral image classification

A novel unsupervised ensemble feature learning method for hyperspectral image classification is proposed in this study. Firstly, we randomly sample multiple discriminative subsets from a hyperspectral image with the novel spatially constrained similarity measurement. Each subset consists of a small amount of representative pixels. Each pixel in the subset was assigned with a latent-subclass/pseudo label. Multiple multinomial logistic regression classifiers are then adopted to build relations between pixels and their latent subclass labels, where each classifier is trained with one subset. Finally, the predicted results of different classifiers for a given pixel are assembled as its ensemble feature. More discriminative features are extracted by the proposed method compared with features extracted by traditional unsupervised methods such as principal component analysis and non-negative matrix factorization. Experimental results on hyperspectral image classification demonstrate the effectiveness of the proposed method.

Robust ensemble feature extraction for solving conditional nonlinear optimal perturbation

Conditional nonlinear optimal perturbation CNOP has been widely applied to predictability and sensitivity studies of nonlinear models. The popular methods of solving CNOP can be divided into two categories: adjoint-based and ensemble-based. Although the adjoint-based method is very accurate, it requires the development of adjoint models. The ensemble-based method is an adjoint-free technique, but either its robustness is weak or its filtering process is dependent on observation or experience to a great extent. In this paper, we propose a robust ensemble-based method to solve CNOP. This method does not need the filtering process and can extract robust features. To demonstrate the validity, the proposed method is applied to the Zebiak-Cane ZC model and compared with the adjoint-based method and other ensemble-based methods. To improve the computational efficiency, we design an OpenMP-based parallelising scheme for the proposed method. Experimental results show that the proposed method can outperform other ensemble-based methods in robustness and the corresponding solution of CNOP significantly approximates the one obtained with the adjoint-based method.

Towards an intelligent framework for multimodal affective data analysis

An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human–computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.

Error-correcting output codes based ensemble feature extraction

This paper proposes a novel feature extraction method based on ensemble learning. Using the error-correcting output codes (ECOC) to design binary classifiers (dichotomizers) for separating subsets of classes, the outputs of the dichotomizers are linear or nonlinear features that provide powerful separability in a new space. In this space, the vector quantization based meta classifier can be viewed as an ECOC decoder, where each learned prototype of a class can be seen as a codeword of the class in the new representation space. We conducted extensive experiments on 16 multi-class data sets from the UCI machine learning repository. The results demonstrate the superiority of the proposed method over both existing ECOC approaches and classic feature extraction approaches. In particular, the decoding strategy using a meta classifier is shown to be more computationally efficient than the linear loss-weighted decoding in state-of-the-art ECOC methods.