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 9 of 9 for “"Ensemble Algorithm"”.
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Boosted ensemble algorithm strategically trained for the incremental learning of unbalanced data
… The purpose of this research is to develop an algorithm capable of incrementally learning from severely unbalanced data. This work introduces three novel ensemble based algorithms derived from the incremental learning algorithm, Learn++. Learn++.NC is designed specifically for incrementally …
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Decoupling methods for the time-dependent Navier-Stokes-Darcy interface model
… Meanwhile, we propose and analyze an efficient ensemble algorithm, which can significantly improve the computational efficiency, for fast computation of multiple realizations of the stochastic Stokes-Darcy model with a random hydraulic conductivity tensor. Furthermore, we utilize the idea of …
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Streaming Random Forests
… and financial applications. Data-stream mining algorithms incorporate special provisions to meet the requirements of stream-management systems, that is stream algorithms must be online and incremental, processing each data record only once (or few times); adaptive to distribution changes; and …
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Detection of malicious content in JSON structured data using multiple concurrent anomaly detection methods
… this research employs is the Random Forest ensemble algorithm. Metrics such as Shannon entropy, n-gram analysis, JSON structure similarity, character string length, and JSON attribute values are utilized. A goal of this research was the detection of attacks at a rate at least better than …
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Heuristic ensembles of filters for accurate and reliable feature selection
… be used for a particular dataset. Thus, an ensemble method that combines the outputs of several individual feature selection methods appears to be a promising approach to address the issue and hence is investigated in this research. This research aims to develop an effective ensemble that …
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Statistical and Machine Learning Models to Predict Programming Performance
… an analysis of the use of machine learning (ML) algorithms to predict performance and is a first attempt to investigate the effectiveness of a variety of ML algorithms to predict introductory programming performance. The ML models built as part of this research are the most effective models so …
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Uplift modeling with multiple treatments
… the new evaluation method, we derive an uplift algorithm named Contextual Treatment Selection (CTS). CTS is a tree-based ensemble algorithm. The trees are built with a splitting criterion designed to directly optimize their uplift performance as measured on the training data. This idea is in …
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Efficient High Order Ensemble for Fluid Flow
<p>"This thesis proposes efficient ensemble-based algorithms for solving the full and reduced Magnetohydrodynamics (MHD) equations. The proposed ensemble methods require solving only one linear system with multiple right-hand sides for different realizations, reducing computational cost and …
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Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring
<p>More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an …