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 648 for “"random forest"”.
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Incremental random forest classifiers in spark
The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents …
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Soil rutting prediction using Random Forest model
Soil rutting in forest operations is a critical phenomenon, characterized by depressions or tracks on the forest floor, often caused by heavy machinery use such as logging equipment. These disturbances can have profound impacts on forest health and ecosystem integrity, disrupting soil structure, …
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Using Random Forest in the field of metabolomics
… selecting and applying data mining techniques Random Forests methodology stands out among data mining techniques, since it can be used for classification, feature extraction, and analysis. Random Forests algorithm has many different customizable parameters that affect the outcome of a …
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Classifying Imbalanced Financial Fraud Data Utilizing Enhanced Random Forest Algorithm
… classifications' precision is low. I developed a random forest assembly that separates fraudulent transactions into tiers of precision. With this approach, 96% of fraudulent transactions are identified, showing an 8% increase in recall when compared to standard approaches. 59% of fraud …
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Novel Random Forest Methods and Algorithms for Autism Spectrum Disorders Research
<p>Random Forest (RF) is a flexible, easy to use machine learning algorithm that was proposed by Leo Breiman in 2001 for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Its superior prediction accuracy has made it the most used algorithms …
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Crystallisation thermodynamics and random forest classification for the prediction of crystallisation outcomes
… crystallisation outcomes, the result showed that random forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for MFA crystals grown in 20 out of the 28 solvents included in this work. Further characterization of the crystals grown in …
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Predicting residential demand: applying random forest to predict housing demand in Cape Town
The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number …
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A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest
… neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection …
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On Mining Time Series Data with Random Forest Models: Perspectives from Classification, Anomaly Detection, and Distance Measures
L'abstract è presente nell'allegato / the abstract is in the attachment
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Random Forest-based detection of cyber-attacks in substation automation systems in the context of IEC 61850 GOOSE communication protocol
… learning techniques and in particular the Random Forest as an ensemble classifier to detect and classify the cyberattacks from other power quality disturbances and normal operation. Furthermore, the thesis addresses the issue of identifying the key features that effectively help in …
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Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data
… studies, educational interventions are often not randomized. Study results often suffer greatly from self-selection bias. Besides the intervention itself, the efficacy and effectiveness of interventions usually interact with a wide range of confounders.</p> <p>In this study, we propose a novel …
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