Voting Approach Research Articles

Development and validation of a soft voting-based model for urban fire risk prediction

Early identification of fire risk helps to mitigate the possible consequences incurred by fire, and meanwhile helps optimize the allocation of rescue resources. In this paper, we propose a comprehensive machine learning-based approach for predicting fire risk. The study area is divided into multiple 1 km × 1 km grid cells, and fire incident records, Point of Interest (POI) data, and meteorological data are geographically mapped to these grid cells. By applying the k-means clustering algorithm to analyze monthly fire occurrences, fire risk levels are assigned to each grid cell. A dataset is constructed with the target variable being the fire risk level and the predictive variables being POI data and meteorological data. After undergoing feature engineering, the dataset is divided into training and testing sets based on a time span. Deep neural networks, Randomforest, and Extreme Gradient Boosting (XGBoost) algorithms are trained on the training set and combined using a soft voting approach to create the final soft voting-based model (SV-based model). The performance of the SV-based model is rigorously evaluated on the testing set, achieving a prediction accuracy of 98.9 %. When evaluated using the AUC and F1-score as metrics, the scores are 0.92 and 0.98, respectively, surpassing other machine learning models used for comparison. The findings of this research contribute to the field of urban fire risk management and provide valuable insights for decision-makers and urban planners in formulating proactive fire prevention strategies.

The prediction of NICU admission and identifying influential factors in four different categories leveraging machine learning approaches

Neonatal mortality is a concerning issue for many families worldwide. With advances in perinatal and neonatal care, neonatal mortality has been markedly reduced. While the Neonatal Intensive Care Unit (NICU) significantly prevents neonatal mortality in many cases, this study utilizes various Machine Learning (ML) models to predict NICU admission based on a real-world dataset of pregnant women to address and improve the raised issue. The dataset features are categorized into four categories: demographics, pregnancy, neonatal, and delivery factors. Six ML models analyzed features in each and all categories (complete case): Support Vector Machine (SVM), Decision Tree (DT), Gaussian Process (GP), Multilayer Perceptron (MLP), as well as ensemble models Random Forest (RF) and Bagging. We assessed the performance of each model in predicting NICU admission based on different categories and complete cases. Then, a Majority Vote (MV) approach determines the most influential category. As a result, neonatal factors emerge as the most predictive for NICU admission. The accuracy and AUC (Area Under the Curve) values for MVs in each category are as follows: demographic (accuracy: 0.78, AUC: 0.78), pregnancy (accuracy: 0.91, AUC: 0.91), neonatal (accuracy: 0.96, AUC: 0.96), and delivery (accuracy: 0.91, AUC: 0.91). Finally, utilizing the TOPSIS method based on evaluation metrics, the best model for each category and the complete case is identified: Bagging for demographic and pregnancy factors, RF for neonatal factors, DT for delivery factors, and SVM for complete case. The outcomes could enhance neonatal care and optimize resource allocation in NICUs on time.

RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data

The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obtained from an open-source website Kaggle and the total number of participants is 3,254. However, this dataset needs a significant class imbalance problem. To address this issue, we utilized Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADAYSN), a technique for oversampling issues. The primary focus of this study centers around developing a stacking and voting approach that exhibits exceptional performance. We propose a stacking ensemble classifier that is more accurate and effective in predicting stroke disease in order to improve the classifier’s performance and minimize overfitting problems. To create a final stronger classifier, the study used three tree-based ML classifiers. Hyperparameters are used to train and fine-tune the random forest (RF), decision tree (DT), and extra tree classifier (ETC), after which they were combined using a stacking classifier and a k-fold cross-validation technique. The effectiveness of this method is verified through the utilization of metrics such as accuracy, precision, recall, and F1-score. In addition, we utilized nine ML classifiers with Hyper-parameter tuning to predict the stroke and compare the effectiveness of Proposed approach with these classifiers. The experimental outcomes demonstrated the superior performance of the stacking classification method compared to other approaches. The stacking method achieved a remarkable accuracy of 100% as well as exceptional F1-score, precision, and recall score. The proposed approach demonstrates a higher rate of accurate predictions compared to previous techniques.

Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances.

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Predicting progression and deciding on the best follow-up techniques for breast cancer patients is difficult because the illness is diverse and characterized by varying relapse risks. Due to its prevalence, breast cancer has become the top cause of mortality among women worldwide, making diagnosis and prognosis particularly challenging areas of medical study. In addition, the fear of a cancer relapse is a major factor influencing cancer patients' quality of life. The study aims to help doctors determine the likelihood of a breast cancer relapse by applying ensemble learning techniques. In this research, artificial neural networks (ANN) and deep neural networks (DNN) ensembled with Weighted averaging, minority, and majority voting approaches have been investigated for performance enhancements on the breast cancer recurrence dataset sourced from the UCI-ML repository. The empirical analysis shows that this ensemble learning-enabled proposed novel approach shows improved accuracy, precision, sensitivity, specificity, and F1-score of 96.21%, 96.59%, 98.84%, 84.62%, and 97.41%, respectively. The findings of this study can aid doctors in making more informed treatment decisions, thereby improving patient outcomes.

Deteriorated image classification model for malayalam palm leaf manuscripts

The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts.

A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.

An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.

Selection of principal variables through a modified Gram–Schmidt process with and without supervision

AbstractIn various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low‐dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful for obtaining sparser but still comparable models without significant loss of information and performance. In the present paper, we propose a voting approach for sparse overall maximisation of variance analogous to PCA and a similar alternative for deriving sparse regression models influenced closely related to the PLSR method. Both cases yield pivoting strategies for a modified Gram–Schmidt process and its corresponding (partial) QR‐factorisation of the underlying data matrix to manage the variable selection process. The proposed methods include score and loading plot possibilities that are acknowledged for providing efficient interpretations of the related PCA and PLS models in chemometric applications.

An Ensemble Voting Approach for Dropout Student Classification Using Decision Tree C4.5, K-Nearest Neighbor and Backpropagation

An Ensemble Voting Approach for Dropout Student Classification Using Decision Tree C4.5, K-Nearest Neighbor and Backpropagation

Hard Voting Approach using SVM, Naïve Bays and Decision Tree for Kurdish Fake News Detection

In today's age of excessive information, the proliferation of fake news has emerged as a significant challenge, eroding the trustworthiness of news sources and posing serious threats to society. With the advent of social media and the internet, false information spreads more easily, necessitating the development of effective methods to identify and prevent its dissemination.While machine learning models have been widely employed to classify text data as authentic or fake, the majority of existing research has primarily focused on well-resourced languages such as English, leaving low-resourced languages like Kurdish largely overlooked. To address this gap, our proposed work introduces a novel approach: a hard voting ensemble method that combines the insights of four weak learners. By fine-tuning these weak learners for optimal performance, our approach achieves enhanced accuracy compared to the current state-of-the-art methods. Specifically, our findings demonstrate the effectiveness of the hard voting approach using a combination of Support Vector Machines (SVM), Decision Trees (DT), and Naive Bayes (NB) classifiers, resulting in an impressive accuracy rate of 89.73%. This outperforms the individual SVM classifier and underscores the potential of ensemble techniques in fake news detection.

Federated Learning for Distributed IIoT Intrusion Detection Using Transfer Approaches

Considering that low-cost and resource-constrained sensors coupled inherently could be vulnerable to growing numbers of intrusion threats, industrial Internet-of-Things (IIoT) systems are faced with severe security concerns. Data sharing for building high-performance intrusion detection models is also prohibited due to the sensitivity, privacy, and high value of IIoT data. This paper presents an anomaly-based intrusion detection system with federated learning for privacy-preserving machine learning in future IIoT networks. To tackle the urgent issue of training local models with non-independent and identically distributed (non-IID) data, we adopt instance-based transfer learning at local. Furthermore, to boost the performance of this system for IIoT intrusion detection, we propose a rank aggregation algorithm with a weighted voting approach. The proposed system achieves superior detection performance with 95.97% and 73.70% accuracy for AdaBoost and Random Forest respectively, outperforming the baseline models by 12.72% and 14.8%.

Ensemble Learning for Disease Prediction: A Review.

Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016-2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.

Optimal -k nearest neighbours based ensemble for classification and feature selection in chemometrics data

There are various machine-learning techniques available for classification and regression tasks. The k-nearest neighbours (k-NN) method is a well-recognized algorithm that is used for both regression and classification problems. It identifies a group of knearest observations to a given test point, reducing the impact of outliers in the training dataset. For regression, the mean value is calculated, while for classification, the majority value is determined. This study proposes a novel ensemble approach that constructs k-NN models using bootstrap samples from the training data and a randomly selected subset of features. Stepwise logistic regression is then applied to the nearest neighbours identified by each k-NN model to estimate the test observations. The final estimation for the test point's response is made through a majority voting approach using the estimates from different k-NN models. The performance of the proposed method is compared to other methods using five benchmark datasets, using Brier score, sensitivity, and accuracy as performance metrics. The results indicate that the proposed ensemble method outperforms the other methods across most of the datasets. Additionally, the proposed ensemble method is used for feature selection and compared with four other feature selection methods using 9 benchmark datasets. The results demonstrate that the proposed method exhibits superior performance compared to the other methods.

MTR-SDL: a soft computing based multi-tier rank model for shoulder X-ray classification.

Deep neural networks (DNN) effectiveness are contingent upon access to quality-labelled training datasets since label mistakes (label noise) in training datasets may significantly impair the accuracy of models trained on clean test data. The primary impediments to developing and using DNN models in the healthcare sector include the lack of sufficient label data. Labeling data by a domain expert are a costly and time-consuming task. To overcome this limitation, the proposed Multi-Tier Rank-based Semi-supervised deep learning (MTR-SDL) for Shoulder X-Ray Classification uses the small labelled dataset to generate a labelled dataset from unable dataset to obtain performance equivalent to approaches trained on the enormous dataset. The motivation behind the suggested model MTR-SDL approach is analogous to how physicians deal with unknown or suspicious patients in everyday life. Practitioners handle these questionable circumstances with the support of professional colleagues. Before initiating treatment, some patients consult with a range of skilled doctors. Patients are treated according to the most suitable professional diagnosis (vote count). In this article, we have proposed a new ensemble learning technique called "Rank based Ensemble Selection with machine learning models" (MTR-SDL) approach. In this technique, multiple machine learning models are trained on a labeled dataset, and their accuracy is ranked. A dynamic ensemble voting approach is then used to tag samples for each base model in the ensemble. The combination of these tags is used to generate a final tag for an unlabeled dataset. Our suggested MTR-SDL model has attained the best accuracy and specificity, sensitivity, precision, Matthew's correlation coefficient, false discovery rate, false positive rate, f1 score, negative predictive value, and false negative rate negative 92.776%, 97.376%, 86.932%, 96.192%, 85.644%, 3.808%, 2.624%, 91.072%, 90.85%, and 13.068% for unseen dataset, respectively. This approach has the potential to improve the performance of ensemble models by leveraging the strengths of multiple base models and selecting the most informative samples for each model. This study results in an improved Semi-supervised deep learning model that is more effective and precise.

Majority voting based on different feature ranking techniques from gene expression

In bioinformatics studies, many modeling tasks are characterized by high dimensionality, leading to the widespread use of feature selection techniques to reduce dimensionality. There are a multitude of feature selection techniques that have been proposed in the literature, each relying on a single measurement method to select candidate features. This has an impact on the classification performance. To address this issue, we propose a majority voting method that uses five different feature ranking techniques: entropy score, Pearson’s correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and t-test. By using a majority voting approach, only the features that appear in all five ranking methods are selected. This selection process has three key advantages over traditional techniques. Firstly, it is independent of any particular feature ranking method. Secondly, the feature space dimension is significantly reduced compared to other ranking methods. Finally, the performance is improved as the most discriminatory and informative features are selected via the majority voting process. The performance of the proposed method was evaluated using an SVM, and the results were assessed using accuracy, sensitivity, specificity, and AUC on various biomedical datasets. The results demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods in the literature.

Anomaly-based intrusion detection system based on feature selection and majority voting

Recently, cyberattacks have been more complex than in the past, as a new cyber-attack is initiated almost every day. Therefore, researchers should develop efficient intrusion detection systems (IDS) to detect cyber-attacks. In order to improve the detection and prevention of the aforementioned cyber-attacks, several articles developed IDSs exploiting machine learning and deep learning. In this paper, a way to find network intrusions using a combination of feature selection and adoptive voting is investigated. NSL-KDD dataset, a high-dimensional dataset that has been widely used for network intrusion detection, is applied in this approach. Feature selection plays an important role for improving accuracy and testing time as it eliminates the less significant attributes from the data set, thus saving computational power and effort. The experimental results show that the proposed approach achieves an accuracy of 86.5% on the NSL-KDD test dataset using an adoptive voting algorithm trained with the selected features. In addition, the time to process each record is 97.5 microseconds, which reflects the proposed model's superior performance. Comparing the proposed model with the existing models in the literature shows that the proposed adaptive voting approach significantly improves intrusion detection accuracy, enhances computational efficiency, and reduces false positives.

Analysis of Image Preprocessing and Binarization Methods for OCR-Based Detection and Classification of Electronic Integrated Circuit Labeling

Automatic recognition and classification of electronic integrated circuits based on optical character recognition combined with the analysis of the shape of their housings are essential to machine vision methods supporting the production of electronic parts, especially small-volume ones in the through-hole technology, characteristic of printed circuit boards. Since such methods utilize binary images, applying appropriate image preprocessing and thresholding methods significantly influences the obtained results, particularly in uncontrolled illumination conditions. Therefore, the examination of various adaptive image binarization algorithms for this purpose is conducted in this paper, together with the experimental verification of the proposed method based on the pixel voting approach.

Disaster tweet classification: A majority voting approach using machine learning algorithms

Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that particular disaster event on social media, primarily when a disaster occurs. They share their opinion through some textual information such as tweets or posts. In this work, we propose a generalized approach for categorizing the informative and non-informative media on Twitter. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events containing people’s opinions on that specific event. We pre-process the information, which converts the tweet information into machine-understandable vectors. Various machine learning algorithms have processed these vectors. We consider the individual performance of each ML algorithm on different disaster datasets upon choosing the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall, and F1-score. We compared our results with existing models in which our proposed model performed better than other existing state of the art models.

Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.