Reviewing ensemble classification methods in breast cancer

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function RBF and Support Vector Machine SVM as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of direct marketing. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for standard datasets of direct marketing. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of direct marketing.

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

The COVID-19 pandemic has necessitated the development of accurate and efficient classification models for diagnosis and prognosis. While deep learning has shown promising results in various medical applications, its combination with ensemble methods, which amalgamate the predictions of multiple classifiers, can further enhance the model's accuracy. This research paper introduces a novel approach called Deep Neural Ensemble Classification (DNEC) to tackle the challenge of Developing an enhanced ensemble model using deep learning algorithms and comparing its performance with existing ensemble methods. The research problem stems from the literature gap, where existing studies primarily focus on single-model approaches, lacking in-depth exploration of ensemble methods for COVID-19 classification. Motivated by the potential improvement in classification accuracy through ensemble methods, this study aims to create a deep neural ensemble classification model to improve the classification accuracy tailored for COVID-19 data. A set of diverse classifiers, including k-nearest neighbour (IBK), decision tree (J48), naïve bayes (NB), support vector machine (SVM), and sequential minimal optimization (SMO), are utilized in the proposed ensemble method. The accuracy improvement of the ensemble classifiers is evaluated using various metrics such as precision, recall, F1 score, confusion matrix, and processing time. The proposed method demonstrates that IBK+SVM+NB emerges as the top-performing deep neural ensemble classifier with an accuracy score of 99.29% and a total run time of 27.61 seconds. The innovative ensemble techniques introduced in this research contribute to the existing body of knowledge by filling the identified literature gap and offering a novel and highly accurate approach for COVID-19 classification.

Random Forests for land cover classification

In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in clinical breast cancer diagnosis.

To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93.0%, respectively. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers. It can be used as a clinical decision support system to assist breast cancer prediction. In practice, the proposed methodology can be further applied to class imbalanced data classification.

Introduction: Low-fdelity prototyping is a key approach in User-Centered Design (UCD), enabling quick and cost-effective exploration of interface ideas before implementation. Objective: Identify and characterize low-fidelity prototyping tools with respect to their functionalities, limitations, and applicability. Methodology: A Systematic Mapping Study (SMS) was conducted. From an initial sample of 1493 publications obtained on Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect, we selected 37 studies after applying inclusion and exclusion criteria. Results: We identified 22 low-fidelity prototyping tools, highlighting Figma, Balsamiq, and PowerPoint as the most frequently cited. Results show limited integration of emerging technologies such as artificial intelligence, reduced functionality in tools exclusively made for low-fidelity prototyping, and scarce incorporation of structured design guidelines, leaving quality assurance primarily as a software professional’s responsibility.

The subject of this article is exploration of methods for identifying the status of a computer system. The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer system anomalous state by analyzing operating system events. The methods used are artificial intelligence, machine learning and ensemble classification methods. The following results were obtained: the methods of identifying the computer systems anomalous state based on ensemble methods were investigated, namely, bagging, boosting, and classifiers: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump to identify a computer system anomalous state. The different classifiers set and classifiers ensembles were developed. Training and cross-validation on each algorithm was performed. The developed classifiers performance has been evaluated. The research suggests an ensemble method of a computer system state classifying based on the J48 decision tree algorithm. Conclusions. The scientific novelty of the obtained results consists in creating an ensemble method for classifying the state of a computer system based on a decision tree, which makes it possible to increase the reliability and speed of classification.

Traditionally, the approach that has been used in the design of pattern classification was to experimentally assess the performance of several classifiers so that the best one would be chosen. Ensemble classifiers is developed to design a such reliable system with improved accuracy. Recognising that each classifier may make different or complementary errors, the idea is to pool the results from all classifiers to find a composite system that would out-perform any individual (base) classifier. In this way, a single complex classifier may be replaced by a set of relatively simple classifiers. Similarly, the idea of ensemble learning is to employ multiple learners and combine their predictions. Most ensemble methods use a single base learning algorithm to produce homogeneous base learners to produce homogeneous ensembles. Some methods use heterogeneous learners to create heterogeneous ensembles. For ensemble methods to be more accurate than any of its members, the base learners must be as accurate and as diverse as possible. Motivated by this ensemble approach, a heterogeneous ensemble model with different algorithms for members' training is proposed. More specifically, three types of classifiers namely the ELM, SVM and RF have been adopted for classification of Malay syllables. In the process of improving ensemble accuracy and stability, different combination methods are explored. These methods vary in their approach to treat the training data, the type of algorithms used, and the combination methods followed. Diversity among the performance of each single classifier in the ensemble classifiers is also analysed and compared with SD, SI, FI, FD, MI and MD configurations. Based on the obtained results, heterogeneous ensemble method has been successfully proven in improving recognition accuracy on the selected Malay syllables.

The problem of class imbalance in machine learning occurs when there is a relatively big disproportional distribution of classes in the data for classification tasks. In many real-world domains, such as in healthcare, finance, and predictive maintenance, the number of data points of a less important class (usually the negative class) is much higher than the class of greater interest (usually the positive or target class). This affects the ability of many learning algorithms to find good classification models. To address that, many approaches for solving this problem have been proposed, prominently including ensemble methods integrated with sampling-based techniques. However, these methods are still prone to the negative effects of sampling-based techniques that alter class distributions via over-sampling or under-sampling, which can lead to overfitting or discarding useful data, respectively, and thus affect performance. In this paper, we propose a new data preprocessing sampling technique dubbed as (sBal) for ensemble methods for binary classification in the case of imbalanced datasets. Our proposed method first turns the imbalanced dataset into several balanced bins/bags. Then multiple base learners are induced on the balanced bags and finally, the classification results are combined using a specific ensemble rule. We evaluated the performance of our proposed method on 50 imbalanced real-world binary datasets and compared its performance with well-known ensemble methods that utilize data preprocessing techniques namely SMOTEBagging, SMOTEBoost, RUSBoost, and RAMOBoost. The results reveal that the proposed method brings considerable improvement in classification performance relevant to the compared methods. We performed statistical significance analysis using Friedman’s non-parametric statistical test with Bergman post-hoc test. The analysis showed that our method performed significantly better than the majority of the methods across many datasets, suggesting a better preprocessing approach than the ones used in compared methods. We also highlight possible extensions to the method that can improve its effectiveness.KeywordsData samplingEnsemble methodsImbalanced datasetsSplit balancingRAMOBoostSMOTEBaggingSMOTEBoost

A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning

Hypertension is a chronic disease and major risk factor for cardiovascular and cerebrovascular diseases that often leads to damage to target organs. The prevention and treatment of hypertension is crucially important for human health. In this paper, a novel ensemble method based on a flexible neural tree (FNT) is proposed to identify hypertension-related active compounds. In the ensemble method, the base classifiers are Multi-Grained Cascade Forest (gcForest), support vector machines (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logical regression, and naïve Bayes (NB). The classification results of nine classifiers are utilized as the input vector of FNT, which is utilized as a nonlinear ensemble method to identify hypertension-related drug compounds. The experiment data are extracted from hypertension-unrelated and hypertension-related compounds collected from the up-to-date literature. The results reveal that our proposed ensemble method performs better than other single classifiers in terms of ROC curve, AUC, TPR, FRP, Precision, Specificity, and F1. Our proposed method is also compared with the averaged and voting ensemble methods. The results reveal that our method could identify hypertension-related compounds more accurately than two classical ensemble methods.

Ml-Kfhe: Multi-Label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter

BackgroundQuality in use (QiU), a stakeholder-centered dimension of software quality encompassing effectiveness, efficiency, satisfaction, and freedom from risk, is essential in evaluating digital systems, particularly in health-related domains. Although QiU has been explored in various fields, its application within connected mental health (CMH) systems remains fragmented and understudied. Given the rapid rise in CMH technologies, ranging from mobile apps to teletherapy platforms, understanding how QiU is conceptualized, evaluated, and reported in this domain has become increasingly urgent.ObjectiveThis study aims to systematically map and synthesize existing research on QiU in CMH applications. It seeks to identify current trends, research gaps, evaluation methods, and the range of technologies examined concerning QiU.MethodsA systematic mapping methodology following the guidelines by Petersen et al will be used. The process includes defining mapping questions, developing a classification scheme, and systematically searching and analyzing peer-reviewed literature from databases—Scopus, PubMed, IEEE Xplore, and ACM Digital Library. Eight mapping questions will guide the analysis, focusing on publication trends, research types, empirical evaluations, QiU characteristics and subcharacteristics, and technologies studied.ResultsAs this paper presents the protocol for an ongoing mapping study, results are not yet available. The literature search and data analysis are scheduled for completion in 2026. Preliminary screening suggests variability in how QiU is defined and evaluated across CMH technologies, highlighting the need for systematic synthesis.ConclusionsThis systematic mapping study will fill a critical gap by providing a comprehensive overview of QiU research in the context of CMH. By organizing and classifying the existing literature, the study will inform future research, support the development of more user-centered CMH tools, and contribute to establishing more consistent evaluation practices in this growing field.International Registered Report Identifier (IRRID)DERR1-10.2196/79611