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.
Results
Showing 1 to 11 of 11 for “"ensemble classifiers"”.
-
Random Projection Optimal Trees Ensemble
Ensemble classifiers, formed by the combination of multiple weak learners, have been shown to outperform ordinary classification methods in that the former decrease bias, variance and/or improve predictions. These classifiers, however, can still result in low prediction performance when used with …
-
Ensemble-based Supervised Learning for Predicting Diabetes Onset
… presents a tool based on a machine learning ensemble for predicting diabetes onset. Ensembles often perform better than a single classifier, and accuracy and diversity have been highlighted as the two vital requirements for constructing good ensemble classifiers. Experiments in this thesis …
-
Multistage neural network ensemble: adaptive combination of ensemble results
… decade, more and more research has shown that ensembles of neural networks (sometimes referred to as committee machines or classifier ensembles) can be superior to single neural network models, in terms of the generalization performance they can achieve on the same datasets. Combining a set of …
-
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 …
-
Machine Learning Approaches and Web-Based System to the Application of Disease Modifying Therapy for Sickle Cell
… and discriminant analysis of SCD datasets, 7 classifiers based on machine learning models are selected representing linear and non-linear methods. After running these classifiers with a single model, the results revealed that a single classifier has provided us with effective outcomes in terms …
-
An image processing decisional system for the Achilles tendon using ultrasound images
… followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an …
-
Development of a Machine Learning Based Fall Detection System for the Elderly and Disabled
… simulated, including Decision Trees, Naïve Bayes Classifiers, Support Vector Machines, KNN and available Ensemble Classifiers. Four experiments were done, with Experiment 1 using the ADXL345 accelerometer with 5-fold cross-validation, Experiment 2 using 10-fold cross-validation, Experiment 3 using …
-
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 …
-
A neuro-genetic hybrid approach to automatic identification of plant leaves
… used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. …
-
Machine learning for automatic classification of remotely sensed data
… from remotely sensed data using machine learning classifiers, it is a general technique that can be applied to any domain. The emphasis of the applicability for this framework being domains that have inadequate training data available.
-
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 …