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 141 for “"random forests"”.
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Streaming Random Forests
… ensemble algorithm, Streaming Random Forests, an extension of the Random Forests algorithm by Breiman, which is a standard classification algorithm. Our algorithm is designed to handle multi-class classification problems. It is able to deal with data streams having an evolving …
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On pruning and feature engineering in Random Forests.
Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for optimizing RF further by enhancing and …
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Large Margin Random Forests On Mixed Type Data
… is thus important. We propose a large margin random forests classification approach based on random forests proximity. Random forests accommodate mixed data types naturally. Large margin classifiers are obtained from the random forests proximity kernel or its derivative kernels. We test the …
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Random Forests Based Rule Learning And Feature Elimination
… feature elimination, based on 1-norm regularized random forests. This approach simultaneously extracts a small number of rules generated by random forests and selects important features. To evaluate this approach, we have applied it to several drug activity prediction data sets, microarray data …
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Budget allocation on differentially private decision trees and random forests
… of differential privacy on decision trees and forests. This thesis focuses on the dynamic privacy budget allocation algorithm to have more control over the consumption of the budget in the data mining techniques. There are three contributions in this thesis. Contribution 1 proposes a …
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Random forests and their application to heteroscedastic drug design data
Random forests are a popular machine learning method that make predictions by ensembling decision trees. They are widely used on tabular data, particularly drug design datasets. Whilst they often give good predictions they are difficult to interpret and the reasons for their successes and failures …
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Mountains, cities, random forests: new and old frontiers in geographical psychology
The present dissertation offers a big data window into the causes and consequences of geo- graphical differences in personality. After an initial introduction to the field and a broad over- view of the relevant literature (Chapter 1), I present an applied tutorial, that describes the typical …
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Overlapped speech and music segmentation using singular spectrum analysis and random forests
… audio streams, and these are classified using random forests into either speech or music. One of the distinct characteristics of this method is the separation of the speech/music key features that lead to improve the classification performance. Nevertheless, that did encounter a few …
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Three Essays on the Interpretability of Random Forests: Methods, Insights, and Innovations
This thesis examines the interpretability of random forests in three essays, focusing on the discussion of established methods, the presentation of new insights and the provision of innovations. The research aims to bridge the gap between traditional statistical methods and random forests, a …
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Quantifying the Effects of Correlated Covariates on Variable Importance Estimates from Random Forests
… of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of …
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are …
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Optimal feature selection and machine learning for high-level audio classification : a random forests approach
… selection procedure is developed to employ the random forests classifier to rank the features according to their importance and reduces the dimensionality of the feature space accordingly. This new technique avoids the trial-and-error approach used by many authors researchers. The implemented …
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Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification
… Machine learning techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of …
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Comparative Analysis of Discrimination and Calibration Accuracy of Discrete Survival, Random Forests, and Neural Networks in Health-Related Survival Prediction Models
… discrete-time survival trees, discrete-time random survival forests, and discrete-time neural networks. The study uses calibration (measured by the prediction error curves) to assess model fit and discrimination (measured by the Concordance index and area under curve) to evaluate predictive …
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Desenvolvimento de uma abordagem híbrida inteligente para estimação de docking molecular entre proteínas utilizando redes de pseudo-convolução e Random Forests
… redes de pseudo-convolução e algoritmos de *Random Forests*. O objetivo foi melhorar a precisão na previsão da afinidade de ligação entre proteínas por meio de uma estratégia baseada em aprendizado de máquina. As redes de pseudo-convolução foram empregadas para processar sequências de …
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Long short-term memory neural networks for predicting corporate credit ratings
… neural network to predict credit ratings, while random forests have been shown to perform better than regular neural networks. As at the beginning of this study, no study had compared the performance of LSTM and random forests despite their reported superior performance. This study compares the …
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