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As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection. [ABSTRACT FROM AUTHOR]

Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model. This paper introduces a stacked generalization scheme specifically designed for nonlinear time series models. Instead of selecting a single model using traditional model selection criteria, our approach stacks several nonlinear time series models from different classes and proposes a new generalization algorithm that minimizes prediction error. To achieve this, we utilize a feed-forward artificial neural network (FANN) model to generalize existing nonlinear time series models by stacking them. Network parameters are estimated using a backpropagation algorithm. We validate the proposed method using simulated examples and a real data application. The results demonstrate that our proposed stacked FANN model achieves a lower error and improves forecast accuracy compared to previous nonlinear time series models, resulting in a better fit to the original time series data. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the way software, platforms, and infrastructure can be acquired by making them available as on-demand services that can be accessed from anywhere via a web browser. Due to its ubiquitous nature Cloud data centers continuously experience fluctuating workloads which demands for dynamic resource provisioning. These workloads are either placed on Virtual Machines (VMs) or containers which abstract the underlying physical resources deployed at the data center. A proactive or reactive method can be used to allot required resources to the workload. Reactive approaches tend to be inefficient as it takes a significant amount of time to configure the resources to meet the change in demands. A proactive approach for resource management is better in meeting workload demands as it makes an appropriate number of resources available in advance to cater to the fluctuations in workload. The success of such an approach relies on the ability of the resource management module of a data center to accurately predict future workloads. Machine Learning (ML) has already proven itself to be very effective in performing prediction in various domains. In this work, we propose an ML meta-classifier based on stacked generalization for predicting future workloads utilising the past workload trends which are recorded as event logs at Cloud data centers. The proposed model showed a prediction accuracy of 98.5% indicating its applicability for the Cloud environment where SLA requirements must be closely adhered to. [ABSTRACT FROM AUTHOR]

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster prevention and control. In this study, the type and characteristics of slope protection structure disease were determined through the field investigation of an expansive soil area, and this information is incorporated into the numerical simulations and works to develop prediction models of slope stability. Four base machine learning (ML) methods are used to capture the relationship between protection structure diseases and factor of safety (FOS). Further, with the help of stacked generalization (SG), four ML models are combined, and the final SG model is used to predict the FOS. The results show that ML methods can effectively utilize this information and achieve excellent prediction results. The proposed SG model exhibits superior accuracy and robustness in predicting FOS compared to other ML methods. With FOS as the regression variable, the main feature contributions are slope height (37.05%) > slip distance of retaining wall (25.43%) > expansive force (18.03%) > slope gradient (12.00%); the coupling relationship among features is also captured by the proposed model. It is concluded that the SG method is particularly suitable for slope stability modeling under small sample conditions. Besides, the SG-based model effectively captures the impact of protection structure diseases on slope stability, enhances the interpretability of the ML model, and provides a reference for the maintenance and repair of the protection structure. [ABSTRACT FROM AUTHOR]

Purpose: Damage detection of civil infrastructures is vital in structural health monitoring; however, traditional methods are time-consuming and sometimes require skilled experts. With the development of machine learning and deep learning techniques, different artificial neural networks are being used to detect structural damages. However, individual networks can have errors in detecting damages and estimating the accuracy of predictions. Thus, ensemble learning techniques play an essential role in providing more reliable results by combining results obtained by individual models. In this study, a novel deep ensemble learning approach is proposed based on the stacked generalization method for detecting damages in the case of structural health monitoring. A distinguishing feature of the proposed approach is the utilization of a multi-headed deep artificial neural network architecture for classification problems, with a neural network as a meta-learner. Method: In the first step, the CNN, CAE, and LSTM models are trained separately to use the latter as input data for the meta-learner. Then, a deep multi-headed MLP neural network is designed as a meta-learner to combine predictions using the stacking ensemble method to enhance the result accuracy. Results: The proposed method is verified by three structures prepared for structural health monitoring validation. The results represent the promising scope of stacking ensemble learning applications for structural health monitoring and damage detection. Conclusion: The findings indicate that employing ensemble learning is more precise than a neural network when it comes to identifying structural damages. [ABSTRACT FROM AUTHOR]

Flow pattern identification (FPI) is crucial for evaluating air entrapment in water pipelines and ensuring the safety of pipeline operations. The presence of two-phase flow in water pipelines not only leads to pressure fluctuations but also induces pipeline vibration. However, current research has primarily focused on using pressure-related signals for FPI, and the analysis of vibration signals in FPI is rare. In this study, FPI in water pipelines is investigated based on convolutional neural networks (CNNs) using high-frequency vibration signals. The information fusion of vibration signals in FPI is newly proposed via the stacked generalization technique. The proposed method is compared with pressure signal-based FPI methods and the effect of signal sampling parameters on FPI accuracy is discussed. The results show that the performance of vibration signals (including axial or radial acceleration signals) outperforms pressure signals in both time and frequency domains. Moreover, the fusion of vibration signals shows the superior results compared to any univariate signals. The duration of sampling has a more significant impact on the results of FPI than the sampling frequency. This study provides a new way that FPI theory is applied to solve air entrapment evaluation in water pipelines. HIGHLIGHTS The high-frequency vibration signal was proposed for flow pattern identification.; Vibration signals outperform pressure signals for flow pattern recognition.; The vibration signals fused by stacked generalization have better performance than single signals.; For vibration signals, sampling duration has a greater impact on the flow pattern identification performance than sampling frequency.;

The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241–259) for regression and binary classification via Python's scikit-learn. Stacking combines multiple supervised machine learners—the "base" or "level-0" learners—into one learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multilayer perceptron). pystacked can also be used as a "regular" machine learning program to fit one base learner and thus provides an easy-to-use application programming interface for scikit-learn's machine learning algorithms. [ABSTRACT FROM AUTHOR]

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate. [ABSTRACT FROM AUTHOR]

Stacked generalization-based heterogeneous ensemble methods combine the prediction of multiple classifiers to improve overall classification performance. Several stacking methods are available in the literature, but the criteria to select the number and type of classifiers are missing. This work analyzes the performance of stacked generalization-based ensemble machine learning methods for high-dimensional datasets. Also, the impact of the classifier selection for the first level ( $ {L_0} $ L 0 ) of the stacked generalization method has been studied. Based on that, the criteria for selecting classifiers at the first level of the stacked generalization method are proposed. So, six stacked generalization approaches are presented and have been analyzed for the thirty high-dimensional datasets. The experiments and results indicate that the performance of stacking strategies based on proposed selection criteria performs better. Also, a comparative study about the choice of homogeneous ensemble classifiers in stacked generalization concerning the use of basic classifiers and fusion of basic with homogeneous ensemble methods has been made. It has been observed that the use of only homogeneous ensemble classifiers is not beneficial in the stacked generalization methods. Also, the performance of stacked generalization based on basic classifiers or a combination of homogeneous ensemble methods with basic classifiers is better than homogeneous ensemble methods. The proposed stacking approach based on a combination of the basic and ensemble classifiers has improved the accuracy 0.72% to 8.46%. The impact of removing redundant and non-relevant features on the proposed stacking approaches has been evaluated. [ABSTRACT FROM AUTHOR]

In online advertising, a change in the publisher's actual status label with every generated click shows the suspicious behaviour of the publisher. Furthermore, only a small proportion of the clicks generated by the publishers are invalid, resulting in class skewness in the dataset and a challenging issue for the conventional classification methods as they get biased towards the outnumbered class. This suspicious behaviour of publishers with an uneven class distribution ratio adversely affects the classifier's performance and increases model complexities. Thus, developing machine-learning methods capable of producing efficacious predictive models towards detecting fraudulent publishers is pivotal. This paper's novel stacked generalization framework comprises two stacked generalization architectures, one for resampling and the second for classification. The framework employs a stacked generalization approach using generalizers to improve the learning model's performance in two steps: first, reducing the error rate of algorithms towards reducing the bias in a learning set. Second, the results obtained through level-0 generalizers are fed as input to the level-1 generalizer with stacked integrated output towards combining the predictions for improving the predictive performance. Broad experimentations are conducted on FDMA 2012 user click dataset using ten-fold cross-validation. The performance of the proposed architecture is generalized by performing experiments on eight other highly imbalanced benchmark datasets, and performance is measured using average precision, recall, and F1-score. Results empirically prove the superiority of the proposed architecture in the publisher's behaviour prediction and classification as legitimate or illegitimate. [ABSTRACT FROM AUTHOR]

Summary: Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H‐channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance. [ABSTRACT FROM AUTHOR]

It is difficult to identify the fluid properties with one single logging curve in a caved-fracture reservoir due to multi-stage tectonism, diagenetic evolution, rapid lithology change, developed fractures, and significant heterogeneity. Therefore, a stack generalization-based method is proposed for improving fluid identification accuracy. First, a traditional method with cross-plots is adopted by coring and logging data, but it is found that the identification effect of the caved-fracture reservoir fluid is poor. Second, based on the cross-plot, a correlation analysis is conducted to select sensitive logging parameters such as resistivity, compensated neutron, density, acoustic, and total hydrocarbon, which reflect changes in fluid properties, as feature vectors for the identification of fluid types in caved-fracture reservoirs. Third, support vector machine, k-nearest neighbor algorithm, decision tree, and stochastic gradient descent classification are selected as the base learners. 80% of the data sample points and the remaining 20% are selected as training and test samples for building the stacking models to verify the prediction accuracy of the model. Finally, a fully connected neural network is used as a meta-learner to control the final output. The experimental results show that the prediction accuracy of the stack generalization method can reach 88%. Compared with the cross-plot and single machine learning discriminant model, the fluid identification method based on the stack generalization model has a better effect and can improve the fluid identification effect of caved-fracture reservoirs in the study area.

Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. In the proposed framework, a set of heterogeneous classifiers are stacked to produce the first-level outputs. Then these outputs are augmented using several combination functions to construct the inputs of the second-level classifier. We conduct a set of extensive experiments on 121 datasets and compare the proposed method with other established and state-of-the-art heterogeneous methods. The results demonstrate that the proposed scheme outperforms many heterogeneous ensembles, and is superior compared to singly tuned classifiers. The proposed method is also compared to several homogeneous ensembles and performs notably better. Our findings suggest that the improvements are even more significant on larger datasets.

Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors’ data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PDF

Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients' cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure. [ABSTRACT FROM AUTHOR]

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%. [ABSTRACT FROM AUTHOR]

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R²logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R²logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R²logloss = 0.952) and realized (TSS = 0.959, R²logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R²logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R²logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R²logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change. [ABSTRACT FROM AUTHOR]

Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the application of conditional imputation methods computationally infeasible. However, the multi-view structure could be leveraged to reduce the complexity and computational load of imputation. We introduce a new imputation method based on the existing stacked penalized logistic regression (StaPLR) algorithm for multi-view learning. It performs imputation in a dimension-reduced space to address computational challenges inherent to the multi-view context. We compare the performance of the new imputation method with several existing imputation algorithms in simulated data sets and a real data application. The results show that the new imputation method leads to competitive results at a much lower computational cost, and makes the use of advanced imputation algorithms such as missForest and predictive mean matching possible in settings where they would otherwise be computationally infeasible. • A new imputation method for multi-view data is introduced. • The new method shows competitive results at a much lower computational cost. • The new method allows state-of-the-art algorithms to be used in much larger data sets than before. [ABSTRACT FROM AUTHOR]

In recent years, neural architecture search (NAS) algorithms based on Evolutionary Computation (EC) have demonstrated immense potential in the automated design of deep neural network architectures, garnering widespread attention in the field of deep learning. Most EC-based NAS algorithms select the best individual based on overall fitness score. However, some eliminated suboptimal individuals may only perform weakly in overall classification performance, but perform well on certain classes. To search valuable suboptimal individuals and prevent them from being eliminated, we propose a multi-population evolutionary NAS algorithm with stacked generalization (MPE-NAS). Each population evolves based on the classification accuracy of different classes. After completing the evolution process, the stacked generalization approach is utilized to fuse the searched architectures. Moreover, an integrated performance predictor based on k-nearest neighbor (KNN) regression, random forest (RF) and support vector machine (SVM) is proposed to alleviate computational cost during architecture performance evaluation. On the CIFAR benchmark dataset, the proposed algorithm is examined and compared with the most advanced algorithms, and its effectiveness is confirmed based on experiments. In addition, the proposed multi-population evolutionary (MPE) search strategy is applied to others EC-based NAS algorithms, and achieves the performance improvement without increasing computational resources. • A novel multi-population evolutionary search strategy is proposed. • A performance predictor was designed using stacked generalization. • The multi-head attention mechanism was employed. [ABSTRACT FROM AUTHOR]

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance. [ABSTRACT FROM AUTHOR]

User-generated content on numerous sites is indicative of users' sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users' feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers' opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications. [ABSTRACT FROM AUTHOR]

A cognitive-analysis of facial features can make facial expression recognition system more robust and efficient for Human-Machine Interaction (HMI) applications. Through this work, we propose a new methodology to improve accuracy of facial expression recognition system even with the constraints like partial hidden faces or occlusions for real time applications. As a first step, seven independent facial segments: Full-Face, half-face (left/right), upper half face, lower half face, eyes, mouth and nose are considered to recognize facial expression. Unlike the work reported in literature, where arbitrarily generated patch type occlusions on facial regions are used, in this work a detailed analysis of each facial feature is explored. Using the results thus obtained, these seven sub models are combined using a Stacked Generalized ensemble method with deep neural network as meta-learner to improve accuracy of facial expression recognition system even in occluded state. The accuracy of the proposed model improved up to 30% compared to individual model accuracies for cross-corpus seven model datasets. The proposed system uses CNN with RPA compliance and is also configured on Raspberry Pi, which can be used for HRI and Industry 4.0 applications which involve face occlusion and partially hidden face challenges. [ABSTRACT FROM AUTHOR]

Cervical cancer is a malignant tumor that threatens women’s health and life. Cervical pathology examination, as the gold standard for cervical cancer diagnosis, provides an important basis for the surgical plan and postoperative follow-up strategy for cervical cancer. Cervical biopsy diagnosis includes normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC). At present, cervical pathology examination still relies on the doctor’s personal clinical experience and subjective judgment, which is time-consuming and may cause misdiagnosis or missed diagnosis. In addition, the current intelligent classification of cervical pathological images still has disadvantages such as imperfect classification system and low classification accuracy. Therefore, this experiment uses the ResNet50 model of the convolutional neural network as the feature extractor, and selects the K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM) classifiers in Machine Learning to perform cervical tissue pathological images Discrimination, the accuracy of the classification results were 85.83%, 80.33%, and 86.67%. In order to further improve the accuracy of the model and enhance the applicability and stability of the model, this experiment proposes the Stacked Generalization (SK) classification model. The first-layer base learner of the SK classification model selects CNN-KNN, CNN-RF, CNN-SVM, and the second-layer classifier selects Multilayer Perceptron (MLP). Among them, MLP makes the final result by learning the classification performance of the base learner for label discrimination, the accuracy of the classification model after ensemble learning is 90.00%. In addition, this experiment uses the Synthetic Minority Oversampling Technique (SMOTE) algorithm to amplify the training samples, and the amplified data set has a classification accuracy of 89.17% under the training of the SK classification model. The results show that the SK classification model in this experiment has a high classification ability for cervical histopathological images, and has good generalization ability and robustness.

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.

Drug−target interactions (DTIs) identification is an important issue of drug research, and many methods proposed to predict potential DTIs based on machine learning treat it as a binary classification problem. However, the number of known interacting drug−target pairs (positive samples) is far less than that of non-interacting pairs (negative samples). Most methods do not utilize these large numbers of negative samples sufficiently, which limits their prediction performance. To address this problem, we proposed a stacking framework named NegStacking. First, it uses sampling to obtain multiple completely different negative sample sets. Then, each weak learner is trained with a different negative sample set and the same positive sample set, and the logistic regression (LR) is used as a meta-learner to adaptively combine these weak learners. Moreover, in the training process, feature subspacing and hyperparameter perturbation are applied to increase ensemble diversity. Finally, the trained model could be used to predict new samples. We compared NegStacking with other methods, and the experimental results show that our model is superior. NegStacking can improve the performance of predictive DTIs, and it has broad application prospects for improving the drug discovery process. The source code and datasets are available at https://github.com/Open-ss/NegStacking. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is one of the most common types of cancer whose incidence has increased dramatically in recent years. In order to diagnose this disease, many parameters must be taken into consideration and mistakes are possible due to human errors or environmental factors. For this reason, in recent decades, Artificial Intelligence has been used by medical practitioners to diagnose this disease. Method: In this applied-descriptive study, the diagnosis of breast cancer using stacked generalization was presented in the form of an ensemble model based on MLP neural network, ID3 decision tree, and support vector machine methods. To improve the performance of the ensemble classification model, a new approach called separator block was used. This block is responsible for identifying instances that cause errors in the classification model. Results: In order to evaluate the accuracy of the proposed method, the Wisconsin database for breast cancer was used. The experimental results showed the superiority of the proposed method over other similar methods. The accuracy of the classification model presented on the WBCD, WDBC, and WPBC datasets from the Wisconsin database was 99.54%, 99.58% and 99.84%, respectively. Conclusion: Data mining algorithms can provide new and more cost-effective systems in the field of health and treatment that can diagnose breast cancer with high accuracy. In this study, modeling based on the stacked generalization technique was of high accuracy in the diagnosis of breast cancer.

Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.

As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kim, Chu, Park and Lee.)

With the continuous growth of international maritime trade, black carbon (BC) emissions from ships have caused great harm to the natural environment and human health. Controlling the BC emissions from ships is of positive significance for Earth’s environmental governance. In order to accelerate the development process of ship BC emission control technologies, this paper proposes a BC emission prediction model based on stacked generalization (SG). The meta learner of the prediction model is Ridge Regression (RR), and the base learner combines four models: Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), Random Forest (RF), and Support Vector Regression (SVR). We used mutual information (MI) to measure the correlation between combustion characteristic parameters (CCPs) and BC emission concentration, and selected them as the features of the prediction model. The results show that the CCPs have a strong correlation with the BC emission concentration of the diesel engine under different working conditions, which can be used to describe the influence of the changes to the combustion process in the cylinder on the BC generation. The introduction of the stacked generalization method reconciles the inherent bias of various models. Compared with traditional models, the fusion model has achieved higher prediction accuracy on the same datasets. The research results of this paper can provide a reference for the research and development of ship black carbon emission control technologies and the formulation of relevant regulations.