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 20 of 38 for “"Ensemble Method"”.
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Machine learning ensemble method for discovering knowledge from big data
… machine learning and data mining to develop new methods and techniques for analysing big data effectively and efficiently. Ensemble methods represent an attractive approach in dealing with the problem of mining large datasets because of their accuracy and ability of utilizing the …
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Optimisation of pedotransfer function models for soil hydraulic properties using an artificial neural network ensemble method
… thesis focusses on the potential and modelling methodologies of Artificial Neural Network (ANN) ensembles as a means by which to construct PTFs. The individual processes of the ensemble modelling procedure are examined herein, and suggestions as to how they may be improved upon, by mathematical, …
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Toward accurate free energy calculations in biomolecular simulation: advances in Markov model reweighting and expanded ensemble method
… into their folded and unfolded conformational ensembles, which are often challenging to obtain from simulations alone due to timescale limitations and force field inaccuracies. This dissertation advances computational tools for molecular design by integrating molecular dynamics (MD) simulations …
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Tree-based Methods for Learning Probability Distributions
… discussed herein are two types of new tree-based methods: a single-tree method and an ensemble method. The new single tree method, the main topic of Chapter 2, is introduced by constructing a generalized Polya tree process, that is, a new Bayesian nonparametric model, equipped with a new flexible …
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Anomaly detection methods for detecting cyber attacks in industrial control systems
… This thesis evaluates multiple anomaly detection methods to quickly and accurately detect ICS cyber attacks. Two fundamental challenges in developing ICS cyber attack detection methods are the lack of historical attack data and the ability of attackers to make their malicious activity appear …
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Mapping the Cellular Landscape of the Brain: A Scalable Approach to Comprehensive Microscopy Data Analysis
… downstream analysis workflows with existing methods. To address the challenge, we developed an end-to-end scalable framework that seamlessly strings each step of the analysis pipeline together, enabling comprehensive fluorescence microscopy data analysis. The framework, termed MorPheT …
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Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk
… machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem with encouraging results. In this thesis, we present and evaluate an ensemble SVM method of modeling …
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Weighted ensemble analysis of extreme precipitation under climate change
… is used to assign weights to climate model ensemble. This weight result is further applied to projected quantile estimates to derive weighted expected values and confidence intervals of future extreme precipitation events under different emission scenarios, these results are further compared …
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Tracing the Precursors and Amplifiers of Conflict in the Information Age: An NLP Inquiry of Tensions, Political Communication, and Misinformation
… new insights but also advancing the field's methodological landscape. First, the study delves into the identification of causal predictors of conflicts. By showcasing the potential of a frame-semantic parser, I am able to quantify the precursors that contribute to conflict and examine the …
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Ensemble of Feature Selection Techniques for High Dimensional Data
… optima.</p> <p>In this thesis we propose an ensemble approach for feature selection, where multiple feature selection techniques are combined to yield more robust and stable results. Ensemble of multiple feature ranking techniques is performed in two steps. The first step involves creating a …
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Translating biomedical research data to knowledge through bioinformatics
… and knowledge discovery, we developed a novel ensemble data analysis method to improve the predictive ability of classic bagging and AdaBoost methods. By evaluating forty-one online datasets, we demonstrate the ability of our ensemble method in increasing predictive accuracy, which could be …
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Heuristic ensembles of filters for accurate and reliable feature selection
… the accuracy and stability of feature selection methods vary considerably when used individually, and yet no rule exists to indicate which one should be used for a particular dataset. Thus, an ensemble method that combines the outputs of several individual feature selection methods appears to be …
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Predicting Startup Success Using Publicly Available Data
… learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 …
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Interpolating Spline Curves of Measures
… is amenable to several different statistical methods. Yet modern data is often not Euclidean in nature. The semantic content of natural images does not have a vector structure; shifting an image one pixel to the right does not perceptibly change it, but its vector representation is very …
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Enhancing Robustness of Neural Network Interatomic Potentials through Sampling Methods and Uncertainty Quantification
… costs compared to quantum mechanical methods. Without relying on predefined interaction parameters, NNIPs offers greater flexibility and adaptability than classical force fields, and can be used for atomistic simulations of complex materials and biological systems. However, NNIPs face …
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Advanced Optimization Techniques For Monte Carlo Simulation On Graphics Processing Units
… The resulting package supports the different ensemble methods for the Monte Carlo simulation, which will allow for the simulation of multi-component adsorption in porous solids. Such an algorithm will have broad applications to the development of novel porous materials for the sequestration of …
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Impacts of climate change on global agricultural land availability
… high and low emission, respectively. Two ensemble methods, Simple Average Method (SAM) and Root Mean Square Error Ensemble Method (RMSEMM), are employed to assemble the regional climate change which attempts to abate the uncertainty involved in global GCM projections. Fuzzy logic, which …
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Non-parametric Bayesian models for structured output prediction
… implementation of a stochastic inference method designed for the infinite hidden Markov model, applied to a computational linguistics task, part-of-speech tagging. We show that mainstream map-reduce frameworks do not easily support highly iterative algorithms. The main contribution of this …
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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability
This thesis explores ensemble methods in machine learning, a technique that builds a predictive model by jointly training simpler base models. It examines three types of ensemble methods: additive models, tree ensembles, and mixtures of experts. Each ensemble method is characterized by a specific …
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Application of Computer Vision Techniques for Railroad Inspection using UAVs
… were used for training and testing and an ensemble method with majority vote scheme was also explored for classification. The second category of learning model used was a multi-layered neural network. The major drawback of this method was, it required a lot of images for training. However, …
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