Ensemble Machine Research Articles

Time-frequency analysis of speech signals using the Stockwell transform for the detection of upper respiratory tract infection

The acoustic properties of speech demonstrate modifications in the presence of different health states. Biomedical engineering has great promise for creating non-invasive diagnostic processes that use speech as a biomarker. The use of speech indications to screen for upper respiratory tract infections (URTIs), such as the common cold, may have potential advantages in terms of limiting transmission. In this study, we have employed the Stockwell transform -based time-frequency (TF) analysis of speech signals for URTI detection. The Stockwell transform is applied on speech signals to derive their TF representation. Using a TF matrix, the various statistics of magnitude and phase are calculated and used as features for classifying speech of healthy speakers and speakers with URTI. The URTIC database is employed for evaluating the proposed features. The utilization of an ensemble of support vector machines (SVM) is proposed as a classification approach to address the issue of class imbalance. The results show that the proposed method produces comparable outcomes to state-of-the-art approaches. The proposed features obtain 66.53% and 64.65% UARs on the development and test partitions of the URTIC database.

Breast Cancer Prediction Using Support Vector Machine Ensemble with PCA Feature Selection Method

Breast Cancer Prediction Using Support Vector Machine Ensemble with PCA Feature Selection Method

Early warning model based on support vector machine ensemble algorithm

Support Vector Machine (SVM) is a powerful machine learning technique often applied in various early warning scenarios. When using SVM for binary classification, its output results can be converted into a probability distribution based on sample categories. Through the analysis of the results, it is found that when the probability of a sample belonging to a positive class is equivalent to the probability of a sample belonging to a negative class, there is a high possibility of misclassification of the sample. To address this issue, this study proposes a new SVM ensemble model construction method. We generate SVM sub-classifiers to extract error prone set, and utilise three different combination rules to construct ensemble model based on the output probability of the samples. The accuracy of the whole data set is improved for the classification accuracy of the easily misclassified data are improved. This research evaluated the model based on Wind turbine SCADA data and UCI benchmark datasets. According to the research results, the accuracy rate of SCADA data is as high as 97.52%, and the accuracy rate of German credit data is 77.50%. Meanwhile, the comparison with other methods also validates the effectiveness of the proposed method.

Optimization of parallel SVM algorithm for big data

Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy.

Brain Damage Detection Using Ensemble Classifier for Children Health Progress

Magnetic Resonance Imaging (MRI) is a medical imaging technique used to detect brain damage, particularly in children. Previous classification techniques for brain damage had low accuracy, complex edge detection, and high noise levels. In this proposed work, use an ensemble classification technique to analyze MRI brain images of 6-10 year old children. The input data is preprocessed using a Median filter to remove noise and improve image quality before being sent to a segmentation technique. Each part of the segmented image, such as tissue, inner layer, overall shape, and texture, is classified based on pixel size. The classification is done using an ensemble of Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) techniques. The ensemble prediction is multiplied with several input values to classify the MRI image, aggregating different layer values. The output classified image is validated and developed using Mat Lab software simulation, with an accuracy of 97.86%. This method can accurately classify various types of brain damage and aid in early detection in children.

Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles.

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A machine uses a single partition (or bag) of k - 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible machines available for a given partition. We find that with increasing k in the k-bag or machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and fraction machines suggest that the fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines.

Monitoring the after-effects of ischemic stroke through EEG microstates.

Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke. In this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset. The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters. Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.

Prediction intervals for carbon dioxide emissions in china by extreme learning machine ensemble based on particle swarm optimization

<p>The emission of carbon dioxide is the major cause of the greenhouse effect, which has a negative impact on human survival and sustainable economic development; therefore, it is very significant for discovering the underlying influential factors of carbon dioxide emissions. In this paper, an extreme learning machine ensemble based on particle swarm optimization approach(PSO-ELMEnsemble) are applied to predict the emission of carbon dioxide, which provide estimated values as well as the corresponding reliability. In addition, the particle swarm optimization approach is used to optimize the connection weights of extreme learning machine. The performance of the proposed PSO-ELMEnsemble is experimentally validated by carbon dioxide emissions data during the period 1978-2016 by using state-of-the-art comparative method, where the proposed approach outperforms the others in achieving the best trade-off between accuracy and simplicity.</p>

Teaching to extract spectral densities from lattice correlators to a broad audience of learning-machines

We present a new supervised deep-learning approach to the problem of the extraction of smeared spectral densities from Euclidean lattice correlators. A distinctive feature of our method is a model-independent training strategy that we implement by parametrizing the training sets over a functional space spanned by Chebyshev polynomials. The other distinctive feature is a reliable estimate of the systematic uncertainties that we achieve by introducing several ensembles of machines, the broad audience of the title. By training an ensemble of machines with the same number of neurons over training sets of fixed dimensions and complexity, we manage to provide a reliable estimate of the systematic errors by studying numerically the asymptotic limits of infinitely large networks and training sets. The method has been validated on a very large set of random mock data and also in the case of lattice QCD data. We extracted the strange-strange connected contribution to the smeared R-ratio from a lattice QCD correlator produced by the ETM Collaboration and compared the results of the new method with the ones previously obtained with the HLT method by finding a remarkably good agreement between the two totally unrelated approaches.

Least squares support vector machine ensemble based on sampling for classification of quality local cattle

Least squares support vector machine ensemble based on sampling for classification of quality local cattle

A Non-destructive Method for the determination of Carbonation Time for Nominal Concrete Cover Depth Using Non-Linear En-semble Prediction

Carbonation, a process involving the reaction of carbon dioxide and moisture, results in the for-mation of powdery calcium carbonate, a critical durability issue causing reinforcement corrosion. The study analyzed carbonation data from coastal and inland buildings in the Turkish Republic of Northern Cyprus, re-vealing higher carbonation rates than anticipated within their lifespan. An artificial intelligence model named Support Vector Machine (SVM) was applied to predict carbonation time (T) to penetrate concrete cover of 25mm in the TRNC. Subsequently used two ensemble techniques, namely Neural Network Ensembles (NNE) and Support Vector Machine Ensembles (SVME) to enhance the performance of the prediction of T. Four performance criteria namely Correlation Coefficient (CC), Root Mean Square Error (RMSE), Correlation Co-efficient (R2), Mean Absolute Error (MAE) was applied to verify the modelling accuracy. The Values of R2 of Ensemble techniques indicated significant increase in the performance, greater than the SVM model. This shows that using ensemble techniques is promising in getting better predictions of carbonation time (T) to penetrate concrete cover. The results obtained showed that NNE and SVME combination demonstrated the best performance under the evaluation criteria of R2 = 0.8721 and R2 = 0.8644 in testing phases respectively in comparison SVM-M1 to SVM-M3.

Bounded exponential loss function based AdaBoost ensemble of OCSVMs

As a commonly used ensemble method, AdaBoost has drawn much consideration in the field of machine learning. However, AdaBoost is highly sensitive to outliers. The performance of AdaBoost may be greatly deteriorated when the training samples are polluted by outliers. For binary and multi-class classifications, there have emerged many approaches to improving the robustness of AdaBoost against outliers. Unfortunately, there are too few researches on enhancing the robustness of AdaBoost against outliers in the case of one-class classification. In this study, the exponential loss function of AdaBoost is replaced by a more robust one to improve the anti-outlier ability of the conventional AdaBoost based ensemble of one-class support vector machines (OCSVMs). Furthermore, based on the redesigned loss function, the update formulae for the weights of base classifiers and the probability distribution of training samples are reformulated towards the AdaBoost ensemble of OCSVMs. The empirical error upper bound is derived from the theoretical viewpoint. Experimental outcomes upon the artificial and benchmark data sets show that the presented ensemble approach is more robust against outliers than its related methods.

Explainable Prognostics Method through Differential Evolved RVR Ensemble of Relevance Vector Machines

Operating experience from various mechanical components indicates that their operating performance depends on non-well known physical mechanisms, while it is likely that various unexpected factors will act as catalysts for reaching the failure point. Therefore, one way to overcome the partially knowledge of physical mechanisms is the use of data-driven methods that estimate the degradation patterns and predict the failure point. Thus, there is a growing need to design and develop new and more sophisticated prognostic technologies that can estimate the remaining useful life of a mechanical component. In this work, a new method for prognostics is proposed that not only provides a prediction over the failure point but also provides an explanation over the rationale behind that prediction. The proposed method utilized tools from artificial intelligence and more specifically relevance vector machines (RVM) and differential evolution (DE). The cornerstone of the method is the assembly of an ensemble comprised of multiple RVM equipped with different kernels, and the subsequent evolution of the ensemble using the differential evolution. DE will provide a set of values for the coefficients of the ensemble. Then based on the coefficients together with their associated RVMs are used to provide an explanation over the prediction. The explanation stems from the kernels themselves as each kernel models different set of properties. The presented method is tested on a set of real-world degradation data taken from a Gas Turbine (GT) propulsion plant.

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

AbstractThe recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter‐antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit‐based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.

I-Biomarker CaDx: A circulating miRNA-based multi-cancer detection tool with explainable AI for breast cancer.

5 Background: Breast cancer is requires early and accurate detection for effective treatment and improved patient outcomes. Current methods, such as mammography and biopsy, have limitations in accuracy, invasiveness, and accessibility. Circulating microRNAs (miRNAs) are promising non-invasive biomarkers for cancer detection, including breast cancer. i-Biomarker CaDx is a technology-agnostic, multi-cancer early detection and diagnosis test, covering 13 cancer types with 99-100% accuracy. This study evaluates i-Biomarker CaDx performance for breast cancer using a publicly available dataset and assesses explainable artificial intelligence (XAI) interpretations. Methods: The dataset (GEO: GSE73002) contains 1,280 breast cancer patients and an equal number of age-matched healthy individuals. Circulating miRNA profiles were measured using microarray technology. Multiple classification algorithms, including an ensemble of decision trees, neural networks, and support vector machines, were employed for feature selection, classification, and XAI. Hyperparameter optimization was performed, and the best classifiers were integrated with weights proportional to their performance into a final model. The performance of i-Biomarker CaDx was evaluated using cross-validation and independent test sets, while the XAI component provided cohort-level and individual-level explanations of the miRNA alterations associated with the diagnostic outcome. Results: i-Biomarker CaDx demonstrated remarkable diagnostic performance in detecting breast cancer, achieving 100% accuracy in the studied dataset. The sensitivity, specificity, positive predictive value, and negative predictive value of i-Biomarker CaDx surpassed those of traditional diagnostic methods such as mammography and biopsy. The ensemble classifier, consisting of decision trees, neural networks, and support vector machines, effectively captured the complex interactions among miRNAs. The XAI component of i-Biomarker CaDx provided valuable insights into the biological relevance of miRNA alterations in breast cancer diagnosis. Cohort-level and individual-level explanations elucidated the miRNA patterns and their associations with breast cancer, enhancing the understanding of the underlying molecular mechanisms. Conclusions: i-Biomarker CaDx, a technology-agnostic multi-cancer early detection and diagnosis tool, has shown exceptional accuracy in breast cancer detection, outperforming traditional diagnostic methods. The integration of explainable artificial intelligence (XAI) facilitates a deeper understanding of miRNA alterations and their associations with breast cancer, contributing to improved interpretation at both the cohort and individual levels. The results of this study demonstrate the potential of i-Biomarker CaDx as a powerful, non-invasive diagnostic tool for breast cancer.

Hybrid Human and Machine Learning Algorithms to Forecast the European Stock Market

This paper explores the power of news sentiment to predict financial returns, particularly the returns of a set of European stocks. Building on past decision support work going back to the Delphi method, this paper describes a text analysis expert weighting algorithm that aggregates the responses of both humans and algorithms by dynamically selecting the best answer according to previous performance. The proposed system is tested through an experiment in which ensembles of experts, crowds, and machines analyzed Thomson Reuters news stories and predicted the returns of the relevant stocks mentioned right after the stories appeared. In most cases, the expert weighting algorithm was better than or as good as the best algorithm or human. The algorithm’s capacity to dynamically select the best answers from humans and machines results in an evolving collective intelligence: the final decision is an aggregation of the best automated individual answers, some of which come from machines and some from humans. Additionally, this paper shows that the groups of humans, algorithms, and expert weighting algorithms have associated with them, particularly, news topics that these groups are good at making predictions from.

Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model

Widespread fear and panic has emerged about COVID-19 on social media platforms which are often supported by falsified and altered content. This mass hysteria creates public anxiety due to misinformation, misunderstandings, and ignorance of the impact of COVID-19. To assist health professionals in addressing this epidemic more appropriately at the onset, sentiment analysis can potentially help the authorities for devising appropriate strategies. This study analyzes tweets related to COVID-19 using a machine learning approach and offers a high-accuracy solution. Experiments are performed involving different machine and deep learning models along with various features such as Word2vec, term-frequency, term-frequency document frequency, and feature fusion of both feature-generating approaches. The proposed approach combines the extra tree classifier and convolutional neural network and uses feature fusion to achieve the highest accuracy score of 99%. The proposed approach obtains far better results than existing sentiment analysis approaches.

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research’s key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference (p-values < 0.05) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction.