Relevance Vector Machine Classifier Research Articles

Modelling of healthcare data analytics using optimal machine learning model in big data environment

AbstractRecent advances in wireless networking, big data technologies, namely Internet of Things (IoT) 5G networks, health care big data analytics, and other technologies in artificial intelligence (AI) and wearables, have supported the progression of intellectual disease diagnosis methods. Medical data covers all patient data such as pharmacy texts, electronic health reports (EHR), prescriptions, study data from medical journals, clinical photographs, and diagnostic reports. Big data is a renowned method in the healthcare sector, with beneficial datasets that are highly difficult, voluminous, and rapid for healthcare providers for interpreting and computing using prevailing tools. This study combines concepts like deep learning (DL) and big data analytics in medical field. This article develops a new healthcare data analytics using optimal machine learning model in big data environment (HDAOML‐BDE) technique. The presented HDAOML‐BDE technique mainly aims to examine the healthcare data for disease detection and classification in the big data environment. For handling big data, the HDAOML‐BDE technique uses Hadoop MapReduce environment. In addition, the HDAOML‐BDE technique uses manta ray foraging optimization‐based feature selection (MRFO‐FS) technique to reduce high dimensionality problems. Moreover, the HDAOML‐BDE method uses relevance vector machine (RVM) model for the healthcare data environment. Furthermore, the arithmetic optimization algorithm (AOA) is utilized for the parameter tuning of the RVM classifier. The simulation results of the HDAOML‐BDE technique are tested on a healthcare dataset, and the outcomes portray the improved performance of the HDAOML‐BDE strategy over recent approaches in different measures.

Estimating remaining useful life of rotating machinery using relevance vector machine and deep learning network

In today’s manufacturing industry, unexpected equipment failure caused by mechanical degradation usually leads to serious losses in availability, reliability, quality, and cost. To handle the issues of fault diagnosis and remaining useful life (RUL) prediction for rotating machinery, this contribution presents a multi-technique fused method for health prognostics using relevance vector machine (RVM) and deep separable convolutional gated recurrent network (DSCGRN). The proposed data-driven method gets rid of handcrafted feature extraction and considers multi-sensor data and their temporal dependencies at different degradation states. Specifically, the method is implemented in two phases: anomaly detection and RUL prediction. In the first phase, a RVM classifier, which distinguishes the state of mechanical degradation as no-obvious-fault-tendency and fault-prone, provides a fault threshold for the full-life-cycle degradation progressions and enables the method to concentrate more on fault-prone stage. In the second phase, a regression estimator (i.e., DSCGRN) is shaped to learn deep-layer feature representation from the raw multi-sensor data, thus accurate RUL estimation can be carried out, especially for the fault-prone condition. The proposed method is validated through the raw multi-sensor data of bearings accelerated degradation tests. It turns out that the proposed method performs superiority in RUL prediction compared with some existing prognostic approaches.

Thermal Image Diseases Identification Using Hybrid Genetic Algorithm with Relevance Vector Machine Classification

For thermal-based applications, images are obtained by an Infrared camera through Plank’s law. Thermal sensitivity is the smallest temperature difference detected by the camera. Thermal-images were captured through the heat emitted from plant leaves. Initially, the Gaussian noise in the medical Infrared (IR) images is pre-processed by the median filter. Then the features from the preprocessed images are obtained through the principal component analysis algorithm. From the extracted features, the optimal features are selected using the Scale Invariant Differential Evolution-based Feature (SIDEF) algorithm. Through the exploitation of the selected features, the hybrid genetic algorithm with Relevance Vector Machine (HGRVMA) classifier classifies the features into diseases and non-diseases. To validate the performance of the proposed algorithm, it is compared with the existing algorithms in terms of metrics such as sensitivity, accuracy, precision, and recall. The validation results prove that the proposed HGRVM algorithm is optimal than the existing algorithms for all the metrics.

Fully Bayesian analysis of the relevance vector machine classification for imbalanced data problem

AbstractRelevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model. Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed‐form solution for the posterior of the weight parameters. We propose two advanced Bayesian approaches for RVM classification, namely the Enhanced RVM and the Reinforced RVM, to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem, which has an arresting skew in data size between classes. First, the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations, especially the non‐convergence of the iterations. Secondly, we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non‐consistent estimations of the original RVM. Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem. The two‐level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter. The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.

An improved deep bagging convolutional neural network classifier for efficient intrusion detection system

In the current trend, the network-based system has substantial jobs, and they have become the targets of attackers. When an intrusion occurs, the security of a computer system is compromised. As a result, we must seek out the best methods for ensuring frameworks. A crucial component of the security management architecture is the intrusion detection system (IDS). To maintain effective network security, the design and implementation of IDS remain an important assessment topic. For intrusion detection, the previous system created an enhanced relevance vector machine (ERVM) classifier. However, intrusion detection is not robust for large-scale intrusion datasets, resulting in a high attack rate. The suggested work developed an improved deep bagging based convolutional neural network (DBCNN) for intrusion detection to address this issue. Preprocessing, feature selection, and classification are three processes included in the proposed framework. The KDD dataset is preprocessed in this stage using the kalman filter method. The feature selection is then carried out using the inertia weight based dragonfly method (IWDA). Finally, the DBCNN classifier successfully identifies interruption assaults. The KDD dataset is used to test the new model. The test results show that the proposed work accomplishes better execution contrasted and the current framework as far as accuracy, precision, recall and f-measure.

Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers.

Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of automatically translated patient health information.Objective: We aimed to develop high-performing Bayesian machine learning classifiers to assist clinical professionals and healthcare workers in assessing the quality and usability of NMT on depressive disorders. The tool did not require any prior knowledge from frontline health and medical professionals of the target language used by patients.Methods: We used Relevance Vector Machine (RVM) to increase generalisability and clinical interpretability of classifiers. It is a typical sparse Bayesian classifier less prone to overfitting with small training datasets. We optimised RVM by leveraging automatic recursive feature elimination and expert feature refinement from the perspective of health linguistics. We evaluated the diagnostic utility of the Bayesian classifier under different probability cut-offs in terms of sensitivity, specificity, positive and negative likelihood ratios against clinical thresholds for diagnostic tests. Finally, we illustrated interpretation of RVM tool in clinic using Bayes' nomogram.Results: After automatic and expert-based feature optimisation, the best-performing RVM classifier (RVM_DUFS12) gained the highest AUC (0.8872) among 52 competing models with distinct optimised, normalised features sets. It also had statistically higher sensitivity and specificity compared to other models. We evaluated the diagnostic utility of the best-performing model using Bayes' nomogram: it had a positive likelihood ratio (LR+) of 4.62 (95% C.I.: 2.53, 8.43), and the associated posterior probability (odds) was 83% (5.0) (95% C.I.: 73%, 90%), meaning that approximately 10 in 12 English texts with positive test are likely to contain information that would cause clinically significant conceptual errors if translated by Google; it had a negative likelihood ratio (LR-) of 0.18 (95% C.I.: 0.10,0.35) and associated posterior probability (odds) was 16% (0.2) (95% C.I: 10%, 27%), meaning that about 10 in 12 English texts with negative test can be safely translated using Google.

Hyperspectral image classification based on composite kernel relevance vector machine

This paper presents a composite kernel Relevance Vector Machine(RVM) algorithm, for enhanced classification accuracy of hyperspectral images. This paper constructs three forms of composite kernels based on properties of kernels. The spatial feature is extracted using multi-scale morphological method from the image after principal components transform. The final classification is achieved by composite kernel RVM classifier. The proposed approach is tested in experiments on AVIRIS data. Compared with spectral kernel RVM, the OA and Kappa coefficient of composite kernel RVM increased obviously. However, the training time dose not increased. Meanwhile, composite kernel RVM has ability to get high accuracy with relative small training set. The proposed method has practical use in hyperspectral imagery classification.

An Efficient Intrusion Detection System Using Improved Bias Based Convolutional Neural Network Classifier

Today’s modern society has faced many challenges due to the rapid digitization and growing number of hackers, which makes the networking-based systems to become a target place for intruders. The attacks may allure the users, and it compromised the whole system and makes the security the biggest challenge. In this regard, the best way to combat the issues is by exploring new ways to defend the network against threats. More recently, Intrusion Detection Systems (IDS) is a key enabling technology in maintaining the novel network security. Indeed, some existing systems utilize Improved Relevance Vector Machine (IVRM) classifier for performing intrusion detection in network-based systems. In this work, feature selection is done by using Gaussian Firefly Algorithm and Improved Relevance Vector Machine (IRVM) based classification is performed according to the selected features. However, for large-scale intrusion dataset, the intrusion detection is not robust; hence, it leads to high attack rates. The proposed system designed an Improved Bias based Convolutional Neural Network (ICNN) for high attack intrusion detection. For embracing high-security factors and enhanced protection, the proposed system performs three phases, such as preprocessing, feature selection, and classification. The first phase employs the KDD dataset and Kalman filtering method followed by feature selection utilizes Inertia Weight based Dragonfly Algorithm (IWDA) and finally identified the intrusion attacks using Improved Bias based Convolutional Neural Network (IBCNN) classifier. In this work, a novel model performed with the KDD dataset. The suggested method evaluated in terms of accuracy, f-measure, recall, and precision for examining performance compared with existing systems.

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier.

Estimation of the quality of food products is vital in determining the properties and validity of the food concerning the baking and other manufacturing processes. This article considers the quality estimation of the wheat bread that is baked under standard conditions. The sensory data are collected in real-time, and the obtained data are analysed using the efficient data analytics to predict the quality of the product. The dataset obtained consists of 300 bread samples prepared in 15 days whose vital physical, chemical, and rheological measures are sensed. The measures of the read are obtained through sensory tools and are gathered as a dataset. The obtained data are generally raw, and hence, the required features are obtained through dimensionality reduction using the Linear Discriminant Analysis (LDA). The processed data and the attributes are given as input to the classifier to obtain final estimation results. The efficient Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier model is developed for this achieving this objective. The proposed quality estimation model is implemented using the MATLAB programming environment with the required setting for the FWRVM classifier. The model is trained and tested with the input dataset with data analysis steps. Some state-of-the-art classifiers are also implemented to compare the evaluated performance of the proposed model. The estimation accuracy is obtained by comparing the number of correctly detected bread classes with the wrongly classified breads. The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.

Fully Bayesian Analysis of Relevance Vector Machine Classification With Probit Link Function for Imbalanced Data Problem

The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated Normal distribution. Compared with the original RVM classification model, our proposed one is a Fully Bayesian approach, and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem.

Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography

The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode. Surface electromyography (sEMG) combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes. sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification. High-order zero-crossing were computed as the sEMG features regarding locomotion modes. Relevance vector machine (RVM) classifier was investigated. Bat algorithm (BA) was used to compute the RVM classifier kernel function parameters. The classification performance of the particle swarm optimization-relevance vector machine (PSO-RVM) and RVM classifiers was compared. The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes: upslope, downgrade, level-ground walking and stair ascent/descent.

An Efficient Multiangle Weight Updated Haralick and Relevance Vector Machine Algorithm for Classifying Diabetic Foot from Medical Thermal Image

Abstract Skin temperature assessment has gained attention in recent years for its ability to detect diabetes-related foot complications. Early detection of the complications can prevent devastating consequences. Hence, in this article, an efficient multiangle weight updated Haralick (MAWH) algorithm–based foot thermal image processing system is proposed for classification of features into diabetic and nondiabetic categories. Initially, the Gaussian noises in the medical infrared footprint images are preprocessed by the median filter. Then, the features from the preprocessed images are processed by the MAWH, primitive tint feature extraction, and convoluted Tamura pattern algorithms. From the extracted features, the optimal features are selected by the genetic algorithm–differential evolution–based feature subset algorithm. By exploiting the selected features, the relevance vector machine classifier classifies the features as diabetic or nondiabetic. To validate the performance of the proposed algorithm, it is compared with existing algorithms. The validation results prove that the proposed algorithm is more optimal than the existing algorithms for all metrics.

Fuzzy relevance vector machine based classification of lung nodules in computed tomography images

AbstractLung cancer is a critical disease with growing death rate, hence, the faster identification and treatment of lung cancer is essential. In medical image processing, the traditional methods like support vector machine, relevance vector machine for classifying cancer tissues are less sensitive to false data and required optimal improvement in classification accuracy. The proposed system of accurate lung cancer classification is obtained by a hybrid fuzzy relevance vector machine (FRVM) classifier with correlation negation ant colony optimization (CNACO) algorithm. This system provides enhanced accuracy and sensitivity by implementing two stages of feature extraction, image thresholding, and tumor segmentation, with a novel feature selection and tumor classification algorithm. The best features are selected by the proposed CNACO algorithm. The selected features are labeled and classified by FRVM classifier. The proposed classification scheme is validated on lung image database consortium and image database resource initiative public database and obtained accuracy of about 98.75%.

A novel shearer cutting pattern recognition model with chaotic gravitational search optimization

The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining. Hence, a new cutting pattern recognition model based on the combination of Relevance Vector Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) is proposed. Initially, the motor operation data, including voltage, current and motor speed, are collected as the detection signal and the RVM classifier based on Bayesian framework is chosen for pattern recognition. In order to optimize the parameters in RVM, which has a great influence on the performance of RVM, the optimization algorithm Gravitational Search Algorithm (GSA) is introduced. Finally, the basic GSA is modified into CGSA with the chaotic mapping for increasing the search diversity of the algorithm. The experimental study demonstrates the advantageous performance of the proposed model even without any feature extraction operations.

Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots

Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection

A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier

Sentimental analysis is the process of identifying the human’s thoughts or feelings. So Many methods have been developed for the sentimental analysis. Machine learning is one of the widely used approaches towards sentiment classification. In this work, Sentimental analysis is done by using Relevance Vector Machine Classifier with Cuckoo Search Optimization. Here Relevance Vector Machine Classifier (RVMC) is combined with Cuckoo Search Optimization (CSO) for better accuracy and performance. Experiment is made with movie and twitter datasets. Accuracy, precision and recall of all other techniques are evaluated. Here the comparison is made among other algorithms. The result shows that RVMC-CSO algorithm gives accuracy and good performance than other algorithm like SVM, ELM and RVM.

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.

Detection of myocardial infarction from vectorcardiogram using relevance vector machine

Myocardial infarction is a coronary artery ailment, and it is characterized by the changes in the morphological features such as the shape of T-wave, Q-wave and ST-segment of ECG signal. In clinical standard, it is a challenging problem to diagnose MI pathology using 12-lead ECG and vectorcardiogram (VCG). VCG has the advantage to record the heart electrical activities in three orthogonal planes (frontal, sagittal and transverse). This paper proposes a new method for automated detection or grading of MI pathology from vectorcardiogram (VCG) signals. The method uses relevance vector machine (RVM) classifier and the multiscale features of VCG signal for MI detection. The multiscale analysis of VCG signal is performed using dual-tree complex wavelet transform. The diagnostic features such as the complex wavelet sub-band (CWS) \(L_{1}\)-Norm (CWS \(L_{1}\)-norm) and the complex wavelet entropy (CWE) are evaluated from the sub-band complex wavelet coefficients of each orthogonal lead of VCG. The RVM classifier is considered to evaluate the performance of the combination of the CWS \(L_{1}\)-norm and the CWE features of VCG. Three different kernel functions such as Gaussian, bubble and Cauchy are used for RVM. The results show that the RVM classifier with Gaussian kernel function has an average accuracy, an average sensitivity and an average specificity values of 99.80, 99.67 and 99.90%, respectively. The performance of RVM classifier is compared with the existing methods for detection of MI from VCG and 12-lead ECG signals.

Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.