Performance Of Individual Classifiers Research Articles

IoT Enabled Mushroom Farm Automation with Machine Learning

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

Cross-project software defect prediction through multiple learning

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.

Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method

Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance and have received little attention. This is due to a significant data imbalance problem that affects the learning attention for the majority and minority classes. To overcome this problem, a resampling method called ENaNSMOTE-Tomek link is proposed. It uses extended natural neighbors to generate samples that fall within the minority class and then applies the Tomek link technique to eliminate instances that contribute to noise. The model combines the ensemble ’bagging’ technique with the proposed resampling technique to improve the quality of generated samples. The performance of individual classifiers and hybrid models on an imbalanced gout dataset taken from the electronic medical records of a hospital is evaluated. The results of the classification demonstrate that the proposed strategy is more accurate than some imbalanced gout diagnosis techniques, with an accuracy of 80.87% and an AUC of 87.10%. This indicates that the proposed algorithm can alleviate the problems caused by imbalanced gout data and help experts better diagnose their patients.

Ensemble Based Approach: Evaluating network and Model Performance Tackling DDOS attack

With in the dynamic realm of cyber threats, distributed denial of service (DDoS) attacks pose a serious threat. They can undermine network infrastructures and bring about service interruptions that cost money. Our research proposes an ensemble-based technique for DDoS attack detection in response to this problem. By combining the strengths of three distinct classifiers—Random Forest, K-Nearest Neighbors (KNN), and Adaboost—we create a powerful ensemble model. To ensure superior performance, we employ a Multi-Layer Perceptron (MLP) for intricate feature extraction and data normalization in the pre-processing stage. Together with individual classifiers, the ensemble's efficiency is carefully evaluated, verifying that it can accurately identify and counteract DDoS attacks. Motivated by the dynamic nature of DDoS attacks and their inability to be defended against by conventional defense mechanisms, our work is the first to apply machine learning to enhance detection. Ensemble approaches hold promise in addressing the evolving DDoS threat landscape because they combine multiple classifiers to enhance overall performance. The research adds a new dimension by combining MLP-based feature extraction with the Adaboost, KNN, and Random Forest classifiers to increase the discriminatory power of the model. Some of our objectives include building an ensemble-based DDoS attack detection system, evaluating individual classifier performance, comparing ensemble performance with individual classifiers, and using data normalization and MLP-based feature extraction. The research is methodically organized, with a literature review, methodology, performance analysis, ensemble approach analysis, and a concluding summary. The outcomes show the value of the recommended ensemble approach and pave the way for more advancements in DDoS attack detection methods, enhancing online service security and availability in the face of evolving cyber threats.

Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction

Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.

The impact of human expert visual inspection on the discovery of strong gravitational lenses

ABSTRACT We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 per cent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ∼25 or Einstein radii less than ∼1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

Integrated structure-based protein interface prediction

BackgroundIdentifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model.ResultsWe describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces.ConclusionsThe integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. Using various artificial intelligence (AI) techniques, ASD can be detected an at earlier stage than with traditional methods. The aim of this study was to propose a machine learning model that investigates ASD data of different age levels and to identify ASD more accurately. In this work, we gathered ASD datasets of toddlers, children, adolescents, and adults and used several feature selection techniques. Then, different classifiers were applied into these datasets, and we assessed their performance with evaluation metrics including predictive accuracy, kappa statistics, the f1-measure, and AUROC. In addition, we analyzed the performance of individual classifiers using a non-parametric statistical significant test. For the toddler, child, adolescent, and adult datasets, we found that Support Vector Machine (SVM) performed better than other classifiers where we gained 97.82% accuracy for the RIPPER-based toddler subset; 99.61% accuracy for the Correlation-based feature selection (CFS) and Boruta CFS intersect (BIC) method-based child subset; 95.87% accuracy for the Boruta-based adolescent subset; and 96.82% accuracy for the CFS-based adult subset. Then, we applied the Shapley Additive Explanations (SHAP) method into different feature subsets, which gained the highest accuracy and ranked their features based on the analysis.

Optimizing the early glaucoma detection from visual fields by combining preprocessing techniques and ensemble classifier with selection strategies

Artificial Intelligence is booming and many issues of research are being explored to improve technical performance in health systems. But also making them suitable for targeted medical practices. Their cost must also be justified by real added value for medical practitioners and patients. Extracting accurate information from datasets usually comes up against the amount of data and its distribution, which greatly affect the performance of the classifiers. Unbalanced classes or insignificant data features do not provide information for classifiers. Medical data like those of visual field (VF) most suffer from these problems. These factors limit the performance of individual classifiers. However, ensemble methods such as the bagging classifier (BC) can overcome these limitations and return good performances. BC is simple to process and very favorable to the combination with dynamic/static selection strategies (BC-DS/SS) which considerably improves its performance. By remaining sensitive to the problem of data distribution, this combination requires a fusion with pre-processing techniques such as feature selection and data rebalancing to be efficient. Thus, combining pre-processing techniques with the BC-DS/SS ensemble classifiers would allow to extract more accurate information from VF datasets. The stake of this classifier combining pre-processing techniques and ensemble methods with selection strategies named C2PEMS2 (C2 relates to Classifier Combining, PEM refers to Pre-processing and Ensemble Methods and S2 refers to Selection Strategies) consists of: (1) optimizing the performances while reducing the over-fitting, (2) saving in processing time and more importantly (3) predicting more efficiently the targeted class which often is the minority in unbalanced datasets. The experiments of our approach on VF datasets allowed to predict early glaucoma with greater efficiency compared to the state of the art.

Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72 ± 3.9% and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.

Predicting Academic Performance of Engineering Students Using Ensemble Method

One of the common problems that most of the engineering institutions face in recent times is poor academic results. The statistics of the IOE semester result show that since 2009 A.D., the average pass percentage has been reducing in an average from 50% to 40% and moving towards decreasing scenarios. The study aims to predict an engineering student's academic performance based on their past educational records, demographic factors, family backgrounds, and other related factors. Firstly, a predictive model is built using the traditional classifiers Decision Tree, SVM, and Linear Regression, which had shown good performance in similar types of study. After that, we have implemented one of the popular ensemble Methods, voting, which is known for improving the individual classifier's performance. Voting classifier combines the predictions of base classifiers by averaging those predictions. The result revealed that the accuracy, precision, recall, and F1 score had been considerably improved by using ensemble voting than that of the individual classifiers. The data used in the study was collected directly from the hard copy personal files of each pass out student of Paschimanchal Engineering Campus, Pokhara between the years 2004 to 2015 AD.

The Rigor and Transparency Index Quality Metric for Assessing Biological and Medical Science Methods.

SummaryThe reproducibility crisis is a multifaceted problem involving ingrained practices within the scientific community. Fortunately, some causes are addressed by the author's adherence to rigor and reproducibility criteria, implemented via checklists at various journals. We developed an automated tool (SciScore) that evaluates research articles based on their adherence to key rigor criteria, including NIH criteria and RRIDs, at an unprecedented scale. We show that despite steady improvements, less than half of the scoring criteria, such as blinding or power analysis, are routinely addressed by authors; digging deeper, we examined the influence of specific checklists on average scores. The average score for a journal in a given year was named the Rigor and Transparency Index (RTI), a new journal quality metric. We compared the RTI with the Journal Impact Factor and found there was no correlation. The RTI can potentially serve as a proxy for methodological quality.

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification.

The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded.

Ensemble Learning Models for Churn Prediction

Customer churn prediction has always been a major problem in telecom industries. Customer retention is always one of the major objectives of any service providing company as maintaining loyal customers has always been cheaper than acquiring new customers. In this paper, we have tried to predict the churn rate of a dataset from a telecom company using some classifiers and then training the same classifiers with ensemble learning models. The ensemble techniques are assumed to yield better results. We have used 42 classifiers from over different like Nearest Neighbors, Decision Tables, Random Forests, etc., which roughly covers almost all the well-known classifiers used in the industry in today’s date. Further, the ensemble techniques are used in our work such as bagging and boosting which are trained on the same classifiers so that we can compare the performance of individual classifiers as well as the same when used as a base classifier. We have extracted the accuracy of the classifiers, True Positive and False Positive rates, f-measure, MCC score, Area Under Curve (AUC) area and Precision-Recall (PRC) area. These measures, not only helped us know which algorithm is more fruitful but also gave us insights about the varying performance. It is observed that, in most of the cases, the classifiers, when combined with either of the ensemble techniques, yield better results. The experimental results reveal that the accuracy of the classifier improves when combined with bagging or boosting.

Fault recognition using an ensemble classifier based on Dempster–Shafer Theory

Aiming at the poor performance of individual classifier in the field of fault recognition, in this paper, a new ensemble classifier is constructed to improve the classification accuracy by combining multiple classifiers based on Dempster–Shafer Theory (DST). However, in some specific cases, especially when dealing with the combination of conflicting evidences, the DST may produce counter-intuitive results and loss its advantages in combining classifiers. To solve this problem, a new improved combination method is proposed to alleviate the conflicts between evidences and a new ensemble technique is developed for the combination of individual classifiers, which can be well used in the design of accurate classifier ensembles. The main advantage of the proposed combination method is that of making the combination process more efficient and accurate by defining the objective weights and subjective weights of member classifiers’ outputs. To verify the effectiveness of the proposed combination method, four individual classifiers are selected for constructing ensemble classifier and tested on Tennessee-Eastman Process (TEP) datasets and UCI machine learning datasets. The experimental results show that the ensemble classifier can significantly improve the classification accuracy and outperforms all the selected individual classifiers. By comparison with other combination methods based on DST and some state-of-the-art ensemble methods, the proposed combination method shows better abilities in dealing with the combination of individual classifiers and outperforms the others in multiple performance measurements. Finally, the proposed ensemble classifier is applied to the fault recognition in real chemical plant.

An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Predicting future stock index price movement has always been a fascinating research area both for the investors who wish to yield a profit by trading stocks and for the researchers who attempt to expose the buried information from the complex stock market time series data. This prediction problem can be addressed as a binary classification problem with two class labels, one for the increasing movement and other for the decreasing movement. In literature, a wide range of classifiers has been tested for this application. As the performance of individual classifier varies for a diverse dataset with respect to different performance measures, it is impractical to acknowledge a specific classifier to be the best one. Hence, designing an efficient classifier ensemble instead of an individual classifier is fetching increasing attention from many researchers. Again selection of base classifiers and deciding their preferences in ensemble with respect to a variety of performance criteria can be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, an integrated TOPSIS Crow Search based weighted voting classifier ensemble is proposed for stock index price movement prediction. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), one of the popular MCDM techniques, is suggested for ranking and selecting a set of base classifiers for the ensemble whereas the weights of the classifiers used in the ensemble are tuned by the Crow Search method. The proposed ensemble model is validated for prediction of stock index price over the historical prices of BSE SENSEX, S&P500 and NIFTY 50 stock indices. The model has shown better performance compared to individual classifiers and other ensemble models such as majority voting, weighted voting, differential evolution and particle swarm optimization based classifier ensemble.

Embedding Undersampling Rotation Forest for Imbalanced Problem.

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

Robust multimodal biometric system based on optimal score level fusion model

Multimodal biometric systems fuse information from multiple modalities to overcome limitations of individual classifiers. Score level fusion of multiple classifiers can effectively combine information from different modalities. However, most of the multimodal biometric systems are impaired by conflicting classifier scores under dynamic environment, which results in degradation in system's robustness and reliability. To address this, we propose a multimodal biometric system based on an optimal score level fusion model. The key idea of this work is to optimally integrate three complementary biometric traits namely iris, finger vein and fingerprint. For this, individual classifier performance is optimized using evolutionary Backtracking Search Optimization Algorithm (BSA). In addition, conflicting beliefs from individual classifiers are resolved using proportional conflict redistribution rules (PCR-6) to obtain a concurrent solution. The system exhibits optimal behaviour under dynamic environment through boosting or suppression of concurrent classifiers and resolving conflicts among discordant classifiers. The proposed biometric system is evaluated over chimeric multimodal datasets created from benchmark images. On an average, we achieve an accuracy of 98.43% and an EER of 1.57%. The proposed biometric system not only outperforms state-of-the-art techniques but also shows directions towards development of an expert multimodal biometric system.

Multiobjective sparse ensemble learning by means of evolutionary algorithms

Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers for a given dataset. The proposed MOSEL method is applied to well-known MNIST datasets and a real-world remote sensing image change detection problem, and several datasets are used to test the performance of the method on this problem. Experimental results based on both MNIST datasets and remote sensing image change detection show that MOSEL performs significantly better than conventional ensemble learning methods.

Particle swarm optimization-based ensemble learning for software change prediction

ContextVarious researchers have successfully established the association between Object-Oriented metrics and change prone nature of a class. However, they actively continue to explore effective classifiers for developing efficient change prediction models. Recent developments have ascertained that ensemble methodology can be used to improve the prediction performance of individual classifiers. ObjectiveThis study proposes four strategies of ensemble learning to predict change prone classes by combining seven individual Particle Swarm Optimization (PSO) based classifiers as constituents of ensembles and aggregating them using weighted voting. MethodThe weights allocated to individual classifiers are based on their accuracy and their ability to correctly predict “hard instances” i.e. classes which are frequently misclassified by a majority of classifiers. Each individual PSO based classifier uses a different fitness function. The ensembles are constructed on the premises that change in fitness functions leads to variation in the results of a search-based algorithm such as PSO. Therefore, it is important to combine them to obtain a better classifier with improved accuracy using the ensemble methodology. ResultsThe proposed strategies of ensemble learning were found effective in predicting software change. The statistical analysis of the results indicates improved performance of the proposed ensemble classifiers as compared to individual classifiers. Furthermore, the results of the proposed voting ensemble classifiers were found competent with those of machine-learning ensemble classifiers for determination of change prone classes. ConclusionThe accuracy and diversity of the individual classifiers were instrumental in the superior performance of the proposed voting ensemble classifiers.