Ensemble-based Learning Research Articles

Deep Ensemble Learning using Transfer Learning for Food Classification

Aim: Deep learning models, such as deep convolutional neural networks (CNNs), have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities. Background: Similarly, ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification. Objective: We suggest an innovative approach to improve the effectiveness of deep learningbased food classification. Method: Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover, to enhance the classification accuracy even more, we explore transfer learning, employing the migration of acquired weights from a single classifier to another (namely, CNNs). This approach expedites the process of learning. Result: Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques, as evidenced by its classification accuracy. Conclusion: The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.

Modernizing Cancer Diagnosis with an Intelligent Model for Lung and Colon Cancer

Cancer is a lethal condition resulting from a mix of genetic mutations and several metabolic irregularities. Lung and Colon Cancer (LCC) are the primary reasons for mortality and impairment in humans. Identifying these malignancies by histopathology is often crucial for deciding the most appropriate therapy. Early discovery of the disease on the side significantly reduces the chances of death. Machine Learning (ML) methods accelerate cancer diagnosis, enabling researchers to analyze more individuals in a smaller time frame and at a reduced fee. The research presented a hybrid ensemble-based feature-extracting system to detect LCC effectively. It combines advanced feature extraction and ensemble-based learning with efficient filtration for datasets of LCC images. The system is assessed using histopathology datasets for the LCC. The research indicates that the hybrid system can accurately diagnose the lungs and colon. Therefore, these models might be used in clinical settings to assist doctors in diagnosing malignancies.

A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection

Customers use reviews as a primary source of information to judge a product or service. Positive reviews help boost companies’ reputations, increasing their revenue by attracting new clients, and increasing the purchasing order size. On the other hand, negative reviews significantly reduce sales, which might be the case due to competitive advantage. Organizations can use fake (i.e., misleading or fraudulent) reviews to generate fast profits by deceiving customers into buying their products. Recently, various methods to assess the legitimacy of reviews have been introduced using advances in machine learning. However, existing methods fall short of achieving highly accurate detection results for unbalanced classes. We aimed to create a spam review identification model using ensemble-based learning while balancing classes using sampling techniques. This article proposes a weighted stacking ensemble model with sampling (WSEM-S) for efficient fake reviews detection. We used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram models to effectively model language data for feature retrieval. The experimental results on three customer reviews datasets: YELPNYC, Deceptive Opinion Spam Corpus (DOSC) v1.4, and Deception datasets show that the proposed model outperforms the conventional machine learning techniques Naïve Bayes, logistic regression, K-nearest neighbor (KNN), random forest, extreme gradient boosting (XGBoost), and convolutional neural network (CNN) as well as the state-of-the-art ensemble models.

Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality.

Cancer is a leading cause of death globally. The majority of cancer cases are only diagnosed in the late stages of cancer due to the use of conventional methods. This reduces the chance of survival for cancer patients. Therefore, early detection consequently followed by early diagnoses are important tasks in cancer research. Gene expression microarray technology has been applied to detect and diagnose most types of cancers in their early stages and has gained encouraging results. In this paper, we address the problem of classifying cancer based on gene expression for handling the class imbalance problem and the curse of dimensionality. The oversampling technique is utilized to overcome this problem by adding synthetic samples. Another common issue related to the gene expression dataset addressed in this paper is the curse of dimensionality. This problem is addressed by applying chi-square and information gain feature selection techniques. After applying these techniques individually, we proposed a method to select the most significant genes by combining those two techniques (CHiS and IG). We investigated the effect of these techniques individually and in combination. Four benchmarking biomedical datasets (Leukemia-subtypes, Leukemia-ALLAML, Colon, and CuMiDa) were used. The experimental results reveal that the oversampling techniques improve the results in most cases. Additionally, the performance of the proposed feature selection technique outperforms individual techniques in nearly all cases. In addition, this study provides an empirical study for evaluating several oversampling techniques along with ensemble-based learning. The experimental results also reveal that SVM-SMOTE, along with the random forests classifier, achieved the highest results, with a reporting accuracy of 100%. The obtained results surpass the findings in the existing literature as well.

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

Data Integrity Attack Detection Using Ensemble-Based Learning for Cyber–Physical Power Systems

The modern power system has evolved into a cyber-physical system with the integration of PMUs, smart meters, substation automation, and wide-area monitoring and control systems. This increases attack surface area and cybersecurity issues, but we can also use these technologies to prevent such scenarios. This paper provides a novel technique to generate and detect data integrity attacks in smart grids. It also gives an optimization algorithm for generating FDIA against state estimation algorithms present at the control center. The formulation for generating AC state estimation attack with full information and limited information along with DC state estimation attacks is given. It further proposes a combining technique for the voting based ensemble learning technique (MVCC) to detect FDIA in smartgrids. The model is then tested on an IEEE 24 bus system and 39 bus new England system by generating false data injection attacks and detecting them. The detection strategy is compared with most of the existing state of the art Machine learning algorithms, ensemble algorithms and conventional weighted least square algorithm and is found to have a better performance.

Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things

With advanced 5G/6G networks, data-driven interconnected devices will increase exponentially. As a result, the Industrial Internet of Things (IIoT) requires data secure information extraction to apply digital services, medical diagnoses, and financial forecasting. This introduction of high-speed network mobile applications will also adapt. As a consequence, the scale and complexity of Android malware are rising. Detection of malware classification is vulnerable to attacks. A fabricated feature can force misclassification to produce the desired output. This article proposes a subset feature selection method to evade fabricated attacks in the IIoT environment. The method extracts application-aware features from a single android application to train an independent classification model. Ensemble-based learning is then used to train the distinct classification models. Finally, the collaborative ML classifier makes independent decisions to fight against adversarial evasion attacks. We compare and evaluate the benchmark Android malware dataset. The proposed method achieved 91% accuracy with 14 fabricated input features.

Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging.

Alzheimer's is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain's anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer's disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer's Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer's disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer's disease.

Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm

Developing a prognostic model to predict an asset’s health condition is a maintenance strategy that increases asset availability and reliability through better maintenance scheduling. Therefore, developing reliable vehicle health predictive models is vital in the aerospace industry, especially considering a safety–critical system such as aircraft. However, one of the significant challenges faced in building reliable data-driven prognostic models is the imbalance dataset. Training machine-learning models using an imbalanced dataset causes classifiers to be biased towards the class with majority samples, resulting in poor predictive accuracy in data-driven models. This problem can become more challenging if the imbalance ratio is extreme and classes overlap. In this paper, a novel approach called Balanced Calibrated Hybrid Ensemble Technique (BACHE) is developed to tackle the severe imbalanced classification problem. The proposed method involves the combination of hybrid data sampling and ensemble-based learning. It uses a cascading balanced approach to transfer a class imbalance problem into a sub-problem by decomposing the original problem into a set of subproblems, each characterized by a reduced imbalance ratio. Then uses a calibrated boosting with a cost-sensitive decision tree to enhance recognition of hard-to-learn patterns, which improves the prediction of the extreme minority class. BACHE is evaluated using a real-world aircraft dataset with rare component replacement instances. Also, a comparative experiment of the proposed approach with other similar existing methods is conducted. The performance metrics used are precision, recall, G-mean, and an area under the curve. The final results show that the proposed model outperforms other similar methods. Also, it can attain an excellent performance on large, extremely imbalanced datasets.

Current Understanding of Alzheimer's Disease on Biomarkers, Magnetic Resonance Imaging Modalities, and Diagnosis, Prevention, and Treatment Approach

Alzheimer's disease (AD), a neurodegenerative disorder in which Activities of Daily Living (ADL) are hampered and steep decline in gross cognitive function is observed, in the early stage of life. AD is characterized by progressive loss and damage to the structure and/or function of neuronal cell, resulting in death of neurons, however, etiology and pathophysiology of the disease are not known in its entirety. The purpose of this article is to understand, analyze, evaluate, and synthesize information in order to provide conclusive, decisive, and actionable information on (1) microscopic features and known etiology, pathophysiology, genes involved, and protein misfolding observed in AD; (2) selection and use of prominent magnetic resonance imaging (MRI) modalities and allied biomarkers to detect and diagnose AD by application of AI techniques; (3) role of preventive intervention (diet and lifestyle) in reducing risk of developing AD, to act on modifiable and correctable risk factors of AD, to manage AD and treatment strategies of AD through the use of pharmacology and therapeutic drugs. Deep learning-based techniques have proven capabilities to learn features automatically to discriminate class effectively. We proposed a method that incorporates features (biomarkers) derived from the structural MRI modality, clinical assessment tools, and personal and demographic quantifiable parameters into a convolution neural network. and further boosted the ensemble-based learning algorithm to improve prediction accuracy. An ensemble-based learning algorithm is then used to integrate weights to improve prediction accuracy.

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes.

Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification

Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spectral image data. In this study, we propose stack-based ensemble-based learning approach to optimize image classification performance. In addition, we integrate the proposed ensemble learning with XGBoost method to further improve its classification accuracy. To conduct the experiment, the Landsat image data has been acquired from Bishoftu town located in the Oromia region of Ethiopia. The current study’s main objective was to assess the performance of land cover and land use analysis using multi-spectral image data. Results from our experiment indicate that, the proposed ensemble learning method outperforms any strong base classifiers with 99.96% classification performance accuracy.

LidarCSNet: A Deep Convolutional Compressive Sensing Reconstruction Framework for 3D Airborne Lidar Point Cloud

Lidar scanning is a widely used surveying and mapping technique ranging across remote-sensing applications involving topological, and topographical information. Typically, lidar point clouds, unlike images, lack inherent consistent structure and store redundant information thus requiring huge processing time. The Compressive Sensing (CS) framework leverages this property to generate sparse representations and accurately reconstructs the signals from very few linear, non-adaptive measurements. The reconstruction is based on valid assumptions on the following parameters- (1) sampling function governed by sampling ratio for generating samples, and (2) measurement function for sparsely representing the data in a low-dimensional subspace. In our work, we address the following motivating scientific questions- Is it possible to reconstruct dense point cloud data from a few sparse measurements? And, what could be the optimal limit for CS sampling ratio with respect to overall classification metrics? Our work proposes a novel Convolutional Neural Network based deep Compressive Sensing Network (named LidarCSNet) for generating sparse representations using publicly available 3D lidar point clouds of the Philippines. We have performed extensive evaluations for analysing the reconstruction for different sampling ratios {4%, 10%, 25%, 50% and 75%} and we observed that our proposed LidarCSNet reconstructed the 3D lidar point cloud with a maximum PSNR of 54.47 dB for a sampling ratio of 75%. We investigate the efficacy of our novel LidarCSNet framework with 3D airborne lidar point clouds for two domains - forests and urban environment on the basis of Peak Signal to Noise Ratio, Haussdorf distance, Pearson Correlation Coefficient and Kolmogorov-Smirnov Test Statistic as evaluation metrics for 3D reconstruction. The results relevant to forests such as Canopy Height Model and 2D vertical profile are compared with the ground truth to investigate the robustness of the LidarCSNet framework. In the urban environment, we extend our work to propose two novel 3D lidar point cloud classification frameworks, LidarNet and LidarNet++, achieving maximum classification accuracy of 90.6% as compared to other prominent lidar classification frameworks. The improved classification accuracy is attributed to ensemble-based learning on the proposed novel 3D feature stack and justifies the robustness of using our proposed LidarCSNet for near-perfect reconstruction followed by classification. We document our classification results for the original dataset along with the point clouds reconstructed by using LidarCSNet for five different measurement ratios - based on overall accuracy and mean Intersection over Union as evaluation metrics for 3D classification. It is envisaged that our proposed deep network based convolutional sparse coding approach for rapid lidar point cloud processing finds huge potential across vast applications, either as a plug-and-play (reconstruction) framework or as an end-to-end (reconstruction followed by classification) system for scalability.

Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery

Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification.

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.

Assessing Advanced Machine Learning Techniques for Predicting Hospital Readmission

Predicting the probability of hospital readmission is one of the most important healthcare problems for satisfactory, high-quality service in chronic diseases such as diabetes, in order to identify needful resources such as rooms, medical staff, beds, and specialists. Unfortunately, not many studies in the literature address this issue. Most studies involve forecasting the probability of diseases. For prediction, several machine learning methods can be implemented. Nonetheless, comparative studies that identify the most effective approaches for the method prediction are also insufficient. With this aim, our paper introduces a comparative study in the literature across five popular methods to predict the probability of hospital readmission in patients suffering from diabetes. The selected techniques include linear discriminant analysis, instance-based learning (K-nearest neighbors), and ensemble-based learning (random forest, AdaBoost, and gradient boosting) techniques. The study showed that the best performance was in random forest whereas the worst performance was shown by linear discriminant analysis.

Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators

Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a challenging task. In this work, we propose an approach to dealing with simulator imperfection from a point of view of functional approximation that can be implemented through a certain machine learning method, such as kernel-based learning adopted in the current work. To this end, we start from considering a class of supervised learning problems, and then identify similarities between supervised learning and variational data assimilation. These similarities found the basis for us to develop an ensemble-based learning framework to tackle supervised learning problems, while achieving various advantages of ensemble-based methods over the variational ones. After establishing the ensemble-based learning framework, we proceed to investigate the integration of ensemble-based learning into an ensemble-based data assimilation framework to handle simulator imperfection. In the course of our investigations, we also develop a strategy to tackle the issue of multi-modality in supervised-learning problems, and transfer this strategy to data assimilation problems to help improve assimilation performance. For demonstration, we apply the ensemble-based learning framework and the integrated, ensemble-based data assimilation framework to a supervised learning problem and a data assimilation problem with an imperfect forward simulator, respectively. The experiment results indicate that both frameworks achieve good performance in relevant case studies, and that functional approximation through machine learning may serve as a viable way to account for simulator imperfection in data assimilation problems.

Deep Learning Ensemble for Hyperspectral Image Classification

Deep learning models, especially deep convolutional neural networks (CNNs), have been intensively investigated for hyperspectral image (HSI) classification due to their powerful feature extraction ability. In the same manner, ensemble-based learning systems have demonstrated high potential to effectively perform supervised classification. In order to boost the performance of deep learning-based HSI classification, the idea of deep learning ensemble framework is proposed here, which is loosely based on the integration of deep learning model and random subspace-based ensemble learning. Specifically, two deep learning ensemble-based classification methods (i.e., CNN ensemble and deep residual network ensemble) are proposed. CNNs or deep residual networks are used as individual classifiers and random subspaces contribute to diversify the ensemble system in a simple yet effective manner. Moreover, to further improve the classification accuracy, transfer learning is investigated in this study to transfer the learnt weights from one individual classifier to another (i.e., CNNs). This mechanism speeds up the learning stage. Experimental results with widely used hyperspectral datasets indicate that the proposed deep learning ensemble system provides competitive results compared with state-of-the-art methods in terms of classification accuracy. The combination of deep learning and ensemble learning provides a significant potential for reliable HSI classification.

Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling

AbstractGridded human population data provide a spatial denominator to identify populations at risk, quantify burdens, and inform our understanding of human-environment systems. When modeling gridded population, the information used for training the model may differ in spatial resolution than what is produced by the model prediction. This case arises when approaching population modeling from a top-down, dasymetric approach in which one redistributes coarse administrative unit level population data (i.e., source unit) to a finer scale (i.e., target unit). However, often overlooked are issues associated with the differing variance across the scale, spatial autocorrelation and bias in sampling techniques. In this study, we examine the effects of intentionally biasing our sampling from the source to target scale within the context of a weighted, dasymetric mapping approach. The weighted component is based on a Random Forest estimator, which is a non-parametric ensemble-based prediction model. We investigate issues of autocorrelation and heterogeneity in the training data using 18 different types of samples to show the variations in training, census-level (i.e., source) and output, grid-level (i.e., target) predictions. We compare results to simple random sampling and geographically stratified random sampling. Results indicate that the Random Forest model is sensitive to the spatial autocorrelation inherent in the training data, which leads to an increase in the variance of the residuals. Sample training datasets that are at a spatial scale representative of the true population produced the best fitting models. However, the true representative dataset varied in autocorrelation for both scales. More attention is needed with ensemble-based learning and spatially-heterogeneous data as underlying issues of spatial autocorrelation influence results for both the census-level and grid-level estimations.

Ensemble Learning Approach via Kalman Filtering for a Passive Wearable Respiratory Monitor

Utilizing passive radio frequency identification (RFID) tags embedded in knitted smart-garment devices, we wirelessly detect the respiratory state of a subject using an ensemble-based learning approach over an augmented Kalman-filtered time series of RF properties. We propose a novel approach for noise modeling using a "reference tag," a second RFID tag worn on the body in a location not subject to perturbations due to respiratory motions that are detected via the primary RFID tag. The reference tag enables modeling of noise artifacts yielding significant improvement in detection accuracy. The noise is modeled using autoregressive moving average (ARMA) processes and filtered using state-augmented Kalman filters. The filtered measurements are passed through multiple classification algorithms (naive Bayes, logistic regression, decision trees) and a new similarity classifier that generates binary decisions based on current measurements and past decisions. Our findings demonstrate that state-augmented Kalman filters for noise modeling improves classification accuracy drastically by over 7.7% over the standard filter performance. Furthermore, the fusion framework used to combine local classifier decisions was able to predict the presence or absence of respiratory activity with over 86% accuracy. The work presented here strongly indicates the usefulness of processing passive RFID tag measurements for remote respiration activity monitoring. The proposed fusion framework is a robust and versatile scheme that once deployed can achieve high detection accuracy with minimal human intervention. The proposed system can be useful in remote noninvasive breathing state monitoring and sleep apnea detection.