Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"ensemble classifier"”.
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A Machine Learning Classifiers Approach for Cardiovascular Disease Diagnosis
… Machine Learning in cardiology and the role that ensemble classifiers can play to help diagnose cardiovascular disease. The computing power and technology available to humans has helped in the development of the application of computers in cardiology. With this development comes a redundancy of …
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Mining time-series data using discriminative subsequences
… of the original series. We use a heterogeneous ensemble classifier on the transformed data. The accuracy of our method is significantly better than the time-series classification benchmark (1-nearest-neighbour with dynamic time-warping distance), and significantly better than the previous best …
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Random Forest-based detection of cyber-attacks in substation automation systems in the context of IEC 61850 GOOSE communication protocol
… and in particular the Random Forest as an ensemble classifier to detect and classify the cyberattacks from other power quality disturbances and normal operation. Furthermore, the thesis addresses the issue of identifying the key features that effectively help in detecting such cyberattacks.
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Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation
… processing and a trained weighted random forests ensemble classifier, generated alerts are both timely and few enough to allow human analyst review of all generated events. We evaluate this design for three levels of attacker ability with a defined threat model. We evaluate our approach with a …
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Time Series classification through transformation and ensembles
… the benchmark for TSC is nearest neighbour (NN) classifiers using Euclidean distance or Dynamic Time Warping (DTW). Though conceptually simple, many have reported that NN classifiers are very diffi�cult to beat and new work is often compared to NN classifiers. The majority of approaches have …
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Machine Learning Approaches and Web-Based System to the Application of Disease Modifying Therapy for Sickle Cell
… clinical staff. This includes finding an optimal classifier that can help to solve the issues with missing values, multi-class datasets, and features selection. For the classification and discriminant analysis of SCD datasets, 7 classifiers based on machine learning models are selected …
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New approaches to modern statistical classification problems
… of the results of applying an arbitrary base classifier on random projections of the feature vectors into a lower-dimensional space. In one special case that we study in detail, the random projections are divided into non-overlapping blocks, and within each block we select the projection …
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Expressions of psychological stress on Twitter: detection and characterisation
… in all four domains better than machine learning classifiers, based on the performance metrics of accuracy, precision, recall, and f-measure. Swearing and sarcasm were also analysed in high-stress and no-stress datasets from the four domains using a Convolutional Neural Network and Multilayer …
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Advancing Radiotherapy Treatment Through Artificial Intelligence-Driven Approaches
… labels. Secondly, a deep-learning and radiomics ensemble classifier was developed for the false positive reduction in the raw mBMs segmentation, to improve the mBMs detection specificity while maintaining a promising sensitivity. (2) Effective SRS management through AI predictions. In this part, …
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Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk
… In this thesis, we present and evaluate an ensemble SVM method of modeling radiation pneumonitis. The method internalizes kernel/model parameter selection into model building and enables feature scaling via Olivier Chapelle's method. We show that the ensemble method provides statistically …
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Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep
… work in this thesis evaluates the performance of ensemble classifiers (Bagging, Boosting, or RusBoosting) using three different validation methods (5-fold, 0.3 hold-out, and proposed one ‘Single Sheep Splitting’) in comparison to three sampling rates (10, 5, 4 Hz), two segmentation approaches …