Bagging Approach Research Articles

Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography.

Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response. Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects. Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q1 (lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q3 (upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q1<LPS<Q3 had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%, P=0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%. An LPS map was defined using 4-dimensional computed tomography-derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials.

Accommodation of ring C expanded deoxyvasicinone in the HDAC inhibitory pharmacophore culminates into a tractable anti-lung cancer agent and pH-responsive nanocarrier

A fragment recruitment process was conducted to pinpoint a suitable fragment for installation in the HDAC inhibitory template to furnish agents endowed with the potential to treat lung cancer. Resultantly, Ring C expanded deoxyvasicinone was selected as an appropriate surface recognition part that was accommodated in the HDAC three-component model. Delightfully, fused quinazolinone 6 demonstrating a magnificent anticancer profile against KRAS and EGFR mutant lung cancer cell lines (IC50 = 0.80–0.96 μM) was identified. Results of the mechanistic studies confirmed that the cell growth inhibitory effects of compound 6 stems for HDAC6 (IC50 = 12.9 nM), HDAC1 (IC50 = 49.9 nM) and HDAC3 inhibition (IC50 = 68.5 nM), respectively. Compound 6 also suppressed the colony formation ability of A549 cells, induced apoptosis, and increased autophagic flux. Key interactions of HDAC inhibitor 6 within the active site of HDAC isoforms were figured out through molecular modeling studies. Furthermore, a pH-responsive nanocarrier (Hyaluronic acid - fused quinazolinone 6 nanoparticles) was designed and assessed using a dialysis bag approach under both normal and acidic circumstances that confirmed the pH-sensitive nature of NPs. Delightfully, the nanoparticles demonstrated selective cell viability reduction potential towards the lung cancer cell lines (A549 lung cancer cell lines) and were found to be largely devoid of cell growth inhibitory effects under normal settings (L929, mouse fibroblast cells).

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.

Fast and Accurate Health Assessment of Lithium-Ion Batteries Based on Typical Voltage Segments

Lithium-ion batteries are widely employed in industries and daily life. Research on the state of health (SOH) of batteries is essential for grasping the performance of batteries, better guiding battery health management, and avoiding safety mishaps caused by battery aging. Nowadays, most research adopts a data-driven artificial intelligence approach to assess SOH. However, the majority of approaches are based on entire voltage, current, or temperature curves. In reality, voltage, current, and temperature are frequently presented in segments, leading to the limited flexibility and slow analysis speed of the traditional techniques. This study solves the problem by dividing the whole voltage curve into many typical kinds of segments with equal timescales based on different typical voltage beginning points. On this foundation, the temporal convolution network (TCN) is used to create a sub-model of SOH estimation for several typical kinds of segments. In addition, the sub-models are fused using the bootstrap aggregating (Bagging) approach to boost accuracy. Finally, this research uses a publicly available dataset from Oxford to demonstrate the effectiveness of the suggested strategy.

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.

The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data

Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering

Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach.

Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware.

Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction

Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction

Tracking Meat of the Sand

Abstract This article explores the skilled arts of tracking and gathering as methods for noticing and theorizing multispecies landscapes in the Kalahari Desert, Botswana. Tracking is typically used to describe a practice of following animals, usually for hunting, whereas gathering primarily refers to the collection of plant and fungal materials. The author presents a case in which these terms have been scrambled during long-term ethnographic field research. The author and his interlocutors tracked the Kalahari desert truffle, an experience that demonstrates how aspects of tracking extend to gathering, but also how the practices are attentive to the movements of landscapes more broadly. This form of tracking attends to multiple spatial and temporal movements that include nonanimals and other nonhumans. It represents a way of noticing the assemblages of more-than-human relations that make up landscapes. These convergences, first identified through tracking, are then explored through the more distributed analytic of gathering. Inspired by Ursula LeGuin’s call to describe stories of gatherers and collectives in her “Carrier Bag Theory of Fiction,” the article argues that thinking tracking through the gathering analytic helps articulate a “carrier bag approach” for understanding landscapes through the gatherings of relations with which they emerge.

Intelligence Diagnostics of Heart Disease Based on Neural Networks Ensemble Use

The problem of constructing an intelligent system for diagnosing heart valve disease is considered. It is shown that the diagnosis is established on the basis of the results of a standard examination, which includes anamnesis, laboratory data, electrocardiogram, echocardiography and Doppler examination. The use of hybrid neural networks of ensemble topology is substantiated for solving the problem. In order to show that the appropriate neural networks for composing an ensemble can be effectively selected from a set of available neural networks, an algorithm for structural-parametric synthesis of hybrid neural networks of ensemble topology is proposed. As for training component neural networks it is used Bagging approach. Accuracy and diversity were used as criteria. The structure of the diagnostic system for recognition of valvular heart disease is presented. The results of research of the developed software are presented. As for training component neural networks, the Baging approach is used. The structure of the diagnostic system for recognition of valvular heart disease is presented. The results of the study of the developed software are presented.

Self-Adaptive bagging approach to credit rating

We propose an enhanced bagging strategy based on self adaptive learning. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complex data structures. The proposed strategy has a unique advantage in dealing with classification tasks (i.e., credit rating in this study) with data on a relatively small sample size but a large number of heterogeneously distributed features. The self-organising learning mechanism makes the traditional bagging strategy more efficient in terms of model structure. Each submodel in the bagging network becomes a self-adapting base learner in credit rating. In order to prove the applicability and to measure the performance of the proposed method, we carried out a validation using three different traditional algorithms over both artificial datasets and real market data. The results of the proposed algorithm are, on average, better in most of the comparative experiments, while maintaining a model structure that is less complex.

An Effective Intrusion Detection System Using Homogeneous Ensemble Techniques

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.

Predicting individual event attendance with machine learning: a ‘step-forward’ approach

ABSTRACT Accurately predicting attendance at live events has important operational and financial implications for the arts, entertainment and sport industries. Advances in machine learning offer the potential to improve these processes. Using 10 rounds of attendance data from 5,946 season ticket holders of one professional football team (i.e. 59,460 decisions), we assess the ability of four machine learning approaches to predict attendance. Our results indicate that two machine learning algorithms, XGBoost and Support Vector Machine (SVM), outperform the most commonly employed methodology for modelling individual sport attendance (i.e. logistic regression), in terms of accuracy, recall, F-score and area under the curve (AUC). Random forest and boosted aggregation (bagging) approaches are also compared. Our results suggest that adopting machine learning methodologies, and in particular, XGBoost and SVM, offers providers of live events an improved ability to understand and predict individual attendance, and insight into which consumers are most receptive to changing attendance decisions.

Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach.

With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate. In this work, we validated the feasibility of EEG-based authentication in a real-world, out-of-laboratory setting using a commercial dry-electrode EEG headset and chronic recordings on a population of 15 healthy people. We used an LSTM-based network with bootstrap aggregating (bagging) to decode our recordings in response to a multitask scheme consisting of performed and imagined motor tasks, and showed that it improved the performance of the standard LSTM approach. We achieved an authentication accuracy, false acceptance rate (FAR), and false rejection rate (FRR) of 92.6%, 2.5%, and 5.0% for the performed motor task; 92.5%, 2.6%, and 4.9% for the imagined motor task; and 93.0%, 1.9%, and 5.1% for the combined tasks, respectively. We recommend the proposed method for time- and data-limited scenarios.

Application of bagging in day-ahead electricity price forecasting and factor augmentation

The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. In such an environment, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating the traceability of the predictor selection procedure, we propose the method of Bootstrap Aggregation (bagging). To forecast day-ahead electricity prices in a multivariate context for six major power markets, we construct a large-scale pure price model and apply the bagging approach in comparison with the popular Least Absolute Shrinkage and Selection Operator (LASSO) estimation method. Our forecasting study reveals that bagging provides substantial forecast improvements on daily and hourly scales in almost all markets over the popular LASSO estimation method. The differentiation in the forecast performances of the two approaches appears to arise, inter alia, from their structural differences in the explanatory variables selection process. Moreover, to account for the intraday hourly dependencies of day-ahead electricity prices, all our models are augmented with latent factors, and a substantial improvement is observed only in the forecasts from models covering a relatively limited number of predictors.

Effects of long-term water reduction and nitrogen addition on fine roots and fungal hyphae in a mixed mature Pinus koraiensis forest

PurposeThe lack of synchronous studies on fine roots and fungal hyphae makes it difficult to gain insight into changes in how mycorrhizal trees search for nutrients under reduced precipitation and increased nitrogen deposition.MethodsWe applied a modified version of the ingrowth bag approach to estimate the community-level responses of fine roots and fungal hyphae to long-term changes in nitrogen and water availability in a mixed mature forest.ResultsWater reduction, nitrogen addition and the two treatments applied in combination decreased root length density, root biomass and fungal hyphal length density, with the lowest values occurring in the combined treatment. Compared with fine roots in the control treatment, fine roots in the water reduction treatment had a thinner diameter, lower branching intensity and a greater specific root length. Fungal hyphae in the combined treatment had significantly greater diameters than in the control treatment at 0–10 cm soil depths. Root length density, root biomass, root branching intensity and hyphal length density significantly decreased with increasing soil depth. In contrast, hyphal diameter increased with increasing soil depth. Fungal hyphal length density was positively related to root length density but negatively related to hyphal diameter.ConclusionForest fine roots and fungal hyphae respond plastically to soil water and nitrogen availability. An integrative and simultaneous understanding of both root and hyphal trait plasticity can provide better insights into the resource acquisition strategies of trees experiencing environmental changes.

Bagging k-dependence Bayesian network classifiers

Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.

Multiclass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging

The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process. Software bugs can be classified according to the severity of the bugs. In this paper a comparative analysis of Decision Tree, Naïve Bayes and Bagging approach is done for the bug severity classification. A comparative analysis of the Naïve Bayes, Decision Tree and Bagging approach is done for the accuracy, precision, recall and F-measure parameters

Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging

An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis

In the world there are so many airline services which facilitate different airline facilities for their customers. Those airline services may satisfy or may not satisfy their customers. Customers cannot express their comments immediately, so airline services provide the twitter blog to give the feedback on their services. Twitter has been increased to develop the quality of services[4]. This paper develop the different classification techniques to improve accuracy for sentiment analysis. The tweets of services are classified into three polarities such as positive, negative and neutral. Classification methods are Random forest(RF), Logistic Regression(LR), K-Nearest Neighbors(KNN), Naïve Baye’s(NB), Decision Tree(DTC), Extreme Gradient Boost(XGB), merging of (two, three and four) classification techniques with majority Voting Classifier, AdaBoost measuring the accuracy achieved by the function using 20-fold and 30-fold cross validation was compassed in the validation phase. In this paper proposes a new ensemble Bagging approach for different classifiers[10]. The metrics of sentiment analysis precision, recall, f1-score, micro average, macro average and accuracy are discovered for all above mentioned classification techniques. In addition average predictions of classifiers and also accuracy of average predictions of classifiers was calculated for getting good quality of services. The result describes that bagging classifiers achieve better accuracy than non-bagging classifiers.