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Showing 1 to 20 of 57 for “"Naïve Bayes"”.

  1. A Sentiment Analysis of "Filipinx" on Twitter Using a Multinomial Naïve Bayes Classification Model

    Taboy, ClarisseThe Graduate School and University Center of The City University of New York2023

    … Twitter containing “Filipinx”, and to train a Naïve Bayes model to classify tweets into three sentiments: positive, neutral, and negative. My methodology takes inspiration from that of four related studies that similarly conducted sentiment analysis on English/Filipino tweets involving various …

    cuny-grad Repository record

  2. Comparing Naïve Bayes Classifiers with Support Vector Machines for Predicting Protein Subcellular Location Using Text Features

    Lam, Yin2010

    … the SVM classifier in EpiLoc and HomoLoc, by a naïve Bayes classifier and by a novel classifier which we call the Mean Weight Text classifier. The Mean Weight Text classifier and the naïve Bayes classifier are simple to implement and execute efficiently. In addition, naïve Bayes classifiers have …

    queens Repository record

  3. Implementation of Naïve Bayes, MLP, and XGBoost algorithms in a data stream setup for predicting hotel booking cancellations

    Ye, K.2024

    tilburg Repository record

  4. Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing

    CHANDRASEKAR, PRIYANGA2016

    … 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 …

    kennesaw Repository record

  5. Machine learning classification techniques for non-intrusive load monitoring

    Chung, JeffersonUniversity of Ontario Institute of Technology2016

    … of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine learning classification technique to automate the load classification process. The co-testing of machine learning classifiers was introduced in this work to improve the classification accuracy of …

    uoit Repository record

  6. System (for) Tracking Equilibrium and Determining Incline (STEADI)

    Silvina, AgastyaNational University of Ireland Maynooth2013

    … (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared …

    maynooth Repository record

  7. Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks

    Fong, Vivian Lin2018

    … that trains and validates configurations of Naïve Bayes and CNN requirements classifiers. Applying our system to a suite of experiments on two well-studied requirements datasets, we recreate or establish the Naïve Bayes baselines and evaluate the impact of CNNs equipped with word embeddings …

    calpoly Repository record

  8. Registres de salut digitals: Tractament de dades i construcció de models de predicció de malaltia

    Cros Roura, SílviaUniversitat Oberta de Catalunya (UOC)2021

    … Regressió logística múltiple, Algoritme de Naïve Bayes, Random Forest, SVM i ANN. S'ha estimat la seva actuació a partir de diferents paràmetres, tals i com són les corbes ROC, la precisió o l'AUC. En excepció de Naïve Bayes, tota la resta de models ha presentat una bona actuació, pel que …

    catalunya Repository record

  9. Machine learning and deep learning techniques for natural language processing with application to audio recordings

    Motitswane, Olorato GlendahNorth-West University (South Africa)2023

    … accuracy of Artificial Neural Network (ANN) and Naïve Bayes classifiers in predicting the employment status of the debtor. To evaluate the performance of the ASR transcription method, word error rate (WER) was used, for text and to compare ANN and Naïve Bayes, the accuracy, recall and F1-Score …

    nwu-za Repository record

  10. Improving prediction of opioid use disorder with machine learning algorithms

    Razzazi, SaharUniversity of Missouri--Kansas City2023

    … models (Artificial Neural Network, Naïve Bayes and XgBoost), with the average recall, precision, f1 score, accuracy and AUC compared using 50 iterations. The results showed that considering “psychotherapeutic dependence or abuse” or “illicit drug other than marijuana dependence or …

    umkc Repository record

  11. Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

    Bostock, J.City University London2014

    … instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were …

    city-london Repository record

  12. Individualized selection of learning objects

    Liu, JianUniversity of Saskatchewan2009

    … learner characteristics. Two weight models, a Bayesian Network Weight Model and a Naïve Bayes Model, were derived from the data collected in the survey. Given a particular learner, both of these models provide a set of personal weights for learning object features required by the individualized …

    sask Repository record

  13. Brief Study of Classification Algorithms in Machine Learning

    Sankara Subbu, Ramesh2017

    … k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an implementation of a Machine …

    cuny Repository record

  14. Extraction of patterns in selected network traffic for a precise and efficient intrusion detection approach

    Rabadia, Priya NaranEdith Cowan University, Research Online, Perth, Western Australia2018

    … algorithms selected for this study were the Naïve Bayes, Markov chain, Apriori and Eclat algorithms The results show the machine learning algorithms applied to the reduced datasets could extract additional patterns that are more precise, compared to their respective full datasets. It was also …

    edithcowan Repository record

  15. Clasificación supervisada basada en redes bayesianas. Aplicación en biología computacional

    Robles Forcada, VíctorInformatica2003

    … EDA en la búsqueda de estructuras de redes Bayesianas para clasificación. Gracias a la aplicación de los algoritmos EDA, se ha desarrollado un nuevo algoritmo de clasificación supervisada denominado Interval Estimation naïve-Bayes y se han mejorado varios de los algoritmos de clasificación …

    upm Repository record

  16. DETECTING DISTRIBUTED DENIAL OF SERVICE ATTACKS IN IPV6 BY USING ARTIFICIAL INTELLIGENCE TECHNIQUES

    Alsadhan, ALiverpool John Moores University2020

    … three different algorithms as its based learner Bayesian network, Decision tree and Naïve Bayes. LWL-Bayesian Network model achieved the highest detection rate of 96.48%. LWL-Naïve Bayes model is the next best model, with an accuracy rate of 96.024%, while the LWL-Decision Tree model had the …

    liverpool-jm Repository record

  17. Tissue classification from electric impedance spectroscopy for haptic feedback in minimally invasive surgery

    Kent, BraydenUniversity of Ontario Institute of Technology2020

    … with least square error, k-Nearest Neighbour and Naïve Bayes using the measured electric impedance and the extracted model parameter values. The thesis culminates in applications of using EIS as part of implementing vibrotactile and force feedback applications involving sets of user trials to …

    uoit Repository record

  18. Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data

    Li, Sichu

    … 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 …

    uno Repository record

  19. Formulating test oracles via anomaly detection techniques

    Almaghairbe, RafigUniversity of Strathclyde2017

    … techniques (mainly Self-training approach - Naïve Bayes with EM clustering algorithm - and Co-training approach - Naïve Bayes) perform far better under both scenarios (two different labelling strategies) as an automated test classifier than Daikon especially when input/output pairs are used …

    strathclyde Repository record

  20. Data Standardization and Machine Learning Models for Histopathology

    Awaysheh, Abdullah MamdouhVirginia Tech2017

    … and serum chemistry data. Three models (using naïve Bayes, neural networks, and C4.5 decision trees) were trained and tested on laboratory results for 40 Normal, 40 IBD, and 40 ALA cats. Diagnostic models achieved classification sensitivity ranging between 63% and 71% with naïve Bayes and …

    vt Repository record

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