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 20 of 188 for “"tree-based"”.
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Topics in Tree-Based Methods
… with emphasis on classification and regression trees. We begin in Chapter 1 by describing novel techniques for growing classification and regression trees designed to induce visually interpretable trees. This is achieved by penalizing splits that extend the subset of features used in a …
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Tree-based Methods for Learning Probability Distributions
… and mainly 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 …
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FP-tree Based Spatial Co-location Pattern Mining
… a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in …
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Ensemble Tree-Based Machine Learning for Imaging Data
… dissertation, we are proposing novel statistical tree-based methods with more efficient and more accurate responses for use in medical imaging applications.</p> <p>In Chapter 2, we introduce a gradient Boosted Trees for Spatial Data (Boost-S) with covariate information.The main innovation of this …
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Advances in Bayesian Hierarchical Modeling with Tree-based Methods
… especially suitable for this task is the P\'olya tree type models. Following a divide-and-conquer strategy, these tree-based methods transform the original task into a series of tasks that are smaller in size and easier to solve while their nonparametric nature guarantees the modeling flexibility …
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Fuzzy Decision Tree-based Inference System for Liver Disease Diagnosis
… can have a lot of potential.</p> <p>Decision trees are used in data mining for classification and regression. They are simple to understand and interpret as they can be visualized. But, one of the disadvantages of decision tree algorithms is that they deal with only crisp or exact values for …
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Essays on Tree-based Methods for Prediction and Causal Inference
… chapter explores new variations of Bayesian tree-based machine learning algorithms. Bayesian Additive Regression Trees (BART) (Chipman et al. 2010) and Bayesian Causal Forests (BCF) (Hahn et al. 2020) are state-of-the-art machine learning methods for prediction and causal inference. A number …
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Tree-based Data Replay for More Efficient LLM Continual Learning
… others. This study introduces an evolutionary tree-based data replay method designed to enhance the efficiency of LLMs’ continual training. It leverages the evolutionary relationships among domain-specific data to inform the replay strategy, selectively excluding similar data from the training …
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A Multi-Language Goal-Tree Based Functional Test Planning System
… However, adopting a new language for hardware-based designs involves adapting the existing design and verification tool suite for the new language. This is a very time consuming and capital intensive process. To ease the adoption of new description languages, it is desirable to develop …
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Imbalanced learning using actuarial modified loss function in tree-based models
… first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel actuarial modified loss functions, namely, weighted sum of squared error …
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OpenKV: LSM-tree-based key-value store for open-channel SSD
Log-structured merge (LSM) tree-based key-value stores, such as LevelDB and RocksDB, have seen great adoption in industry due to their high write speed. However, one major issue with LSM-based databases is the high write amplification. The root cause of this problem is the LSM tree structure that …
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Enhancement of digital elevation models using tree-based ensemble machine learning algorithms
… is needed. This research proposes an explainable tree-based ensemble feature-level fusion framework for enhancing satellite DEMs using Cape Town, South Africa as a case study. The enhancement methodology combines elevation and terrain features data alignment (co-registration and resampling) with …
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Performance Analysis of a Binary-Tree-Based Algorithm for Computing Spatial Distance Histograms
… density map which is implemented via a quad tree index. An algorithm having density maps implemented via binary tree is proposed in this thesis. After carrying out many experiments and analysis of the data, we figure out that although the binary tree approach seems efficient in earlier stage, …
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Benefits of a Tree-Based model for stock selection in a South African context
… studies the use of Classification and Regression Tree (CART) models for stock selection within the South African context, with the focus being on the period from when the Global Financial Crisis began in early 2007 until December 2012. By utilising four types of portfolios, a CART model is …
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Extending paratreet, a framework for spatial tree based algorithms, with gpu kernels and load balancers
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Hoeffding-Tree-Based Learning from Data Streams and Its Application in Online Voltage Security Assessment
… (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming …
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Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition
… this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have arbitrary number of children. Compared with the Binary …
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Analysis Of Type And Severity Of Traffic Crashes At Signalized Intersections Using Tree-based Regression And Ordered Probit Models
… One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by …
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