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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Comparison Of Sampling Methods For Predicting Wine Quality Based On Physicochemical Properties, Robert Burigo, Scott Frazier, Eli Kravez, Nibhrat Lohia
Comparison Of Sampling Methods For Predicting Wine Quality Based On Physicochemical Properties, Robert Burigo, Scott Frazier, Eli Kravez, Nibhrat Lohia
SMU Data Science Review
Using the physicochemical properties of wine to predict quality has been done in numerous studies. Given the nature of these properties, the data is inherently skewed. Previous works have focused on handful of sampling techniques to balance the data. This research compares multiple sampling techniques in predicting the target with limited data. For this purpose, an ensemble model is used to evaluate the different techniques. There was no evidence found in this research to conclude that there are specific oversampling methods that improve random forest classifier for a multi-class problem.
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
SMU Data Science Review
This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
SMU Data Science Review
In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing …