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 “"Naïve Bayes classifier"”.
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Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing
… we have many existing classifying approaches, Naïve Bayes Classifier is good at classification because of its simplicity and effectiveness. The aim of this paper is to identify the impact of preprocessing the dataset on the performance of a Naïve Bayes Classifier. Naïve Bayes Classifier is …
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Comparing Naïve Bayes Classifiers with Support Vector Machines for Predicting Protein Subcellular Location Using Text Features
… and apply a support vector machine (SVM) classifier to classify proteins into their respective locations. Both EpiLoc and HomoLoc’s prediction accuracy is comparable to that of state-of-the-art protein location prediction systems. However, in addition to accuracy, other factors such as …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 classes containing 136 …
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Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach
… Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies …
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TOWARDS UNDERSTANDING MODE-OF-ACTION OF TRADITIONAL MEDICINES BY USING IN SILICO TARGET PREDICTION
… Principle”, was modelled via two models: a Naïve Bayes Classifier and a Random Forest Classifier. Chapter 2 discovered the relationship of 46 traditional Chinese medicine (TCM) therapeutic action subclasses by mapping them into a dendrogram using the predicted targets. Overall, the most …
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Machine and Deep Learning Approach for Type 2 Diabetes Prediction Using the CDC’s BRFSS Dataset: A Retrospective Analysis
… (ML) and neural network or multilayer perceptron classifier (NN) model(s) and test their performance on predicting the risk for T2DM. A copy of the dataset was transformed to have balanced classes in the outcome variable to allow further comparison of performance for each predictive model when …
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Semantic deontic modeling and text classification for supporting automated environmental compliance checking in construction
… Different text classification methods such as naïve Bayes classifier (NB), support vector machines (SVM), and maximum entropy (ME), were studied and empirically evaluated in the context of construction contract text classification. Different preprocessing and feature selection methods were …
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E-banking operational risk assessment. A soft computing approach in the context of the Nigerian banking industry.
… Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk …
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E-banking operational risk assessment. A soft computing approach in the context of the Nigerian banking industry.
… Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk …