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 17 of 17 for “"recursive feature elimination"”.
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Enhancing Telecom Churn Prediction: Adaboost with Oversampling and Recursive Feature Elimination Approach
… Lastly, the third set combines oversampling with recursive feature selection to enhance the model's performance further.</p> <p>The results demonstrate that the Adaptive Boost classifier, implemented with oversampling and recursive feature selection, outperforms the other 14 techniques. It …
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Analysis of Chemical Elements in Basalts using Mislabeled Data, a Machine Learning Approach
… tectonic settings. An exploration of new features is done using Logistic Regression and Random Forest to discover any new elements of interest. The models were used with other tools, such as recursive feature elimination and permutations, to increase reliability. Among the scarcely …
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Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft
… and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a …
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Improving prediction of opioid use disorder with machine learning algorithms
… an improvement to these studies by utilizing two feature selection techniques, Boruta and Recursive Feature Elimination (RFE), to identify the best predictor through a majority voting system. The selected feature, combined with 10 demographic, socioeconomic, physical, and psychological predictors …
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Enhancing Algorithmic Early Warning Systems with Dynamic Selection to Predict High School Graduation Outcomes
… are needed. By employing methods such as Recursive Feature Elimination (RFE) and Cross Validation on traditional and modern predictive algorithms, this study demonstrates how advanced data techniques can improve the accuracy and reliability of these systems while allowing for far more …
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CLASSIFICATION OF PATIENTS WITH PAROTID CANCER USING DYNAMIC CONTRAST AND DIFFUSION EXAMINATIONS WITH RADIOMICS AND MACHINE LEARNING TECHNIQUES
… In total, 13 radiomics features were extracted from DWI with 11 B values and dynamic contrast-enhanced T1-weighted sequence. The intensity of association between features and type of tumor class (A, W and M) has been evaluated using three different features importance …
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Leveraging Machine Learning and Causal Inference for Loan Default Prediction
… essential. The research introduces two different Recursive Feature Elimination (RFE) variations based on: (1): occlusion sensitivity and (2): double machine learning (DML), each providing a unique view of feature engineering. These RFE methods were then coupled with machine learning models such as …
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Algorithms in comparative genomics
… disease higher than the chi-square, SVM, and SVM Recursive Feature Elimination (SVM-RFE).
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Forensic research on detecting seam carving in digital images
… proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A …
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Feature selection for cancer classification using microarray gene expression data
… 2009) and SVM-RFE (support vector machine with recursive feature elimination) (Guyon et al., 2002), the proposed method is shown to be more effective and sensitive to differentially expressed genes. In the simulation study, the proposed method has much higher recovery rate than the other two …
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A Machine Learning Classifiers Approach for Cardiovascular Disease Diagnosis
… platform were used. The data was cleaned, and 5 feature reduction techniques were investigated. Here, in addition a statistical unbiased ensemble feature reduction is proposed by imposing a unitary weight on all intersecting features. This Thesis study showed that by considering only 7 features, …
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Modelling prognostic trajectories in Alzheimer’s disease
… tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… of data in the classification stage. The feature selection method is applied to choose features for classification. The obtained classifiers are used in the third approach for selection of models using Pareto optimality. The second approach is implemented using three steps; namely, …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… of data in the classification stage. The feature selection method is applied to choose features for classification. The obtained classifiers are used in the third approach for selection of models using Pareto optimality. The second approach is implemented using three steps; namely, …
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Alternative Analytical and Experimental Procedures to Explore Rumen Fermentation as Driven by Nutrient Supplies
… and limitations of mixed-model meta-analysis, recursive feature elimination (RFE), and additive Bayesian networking (ABN) in identifying relationships among diet, rumen, and milk performance variables. Both mixed-models and ABN agreed upon most of the variables and relationships identified …
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Development of hay yield monitoring systems
… selected from candidate VIs and texture features, to predict hay biomass. Model performance was evaluated using measures like R2 and RMSE, and models including VIs and texture features provided R2 values from 0.31 to 0.68. Significant findings included correlations between VIs and dry …