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Dissertation/Thesis Abstract

Different Sampling Techniques in Neural Networks
by Micael, Deborah, M.S., The George Washington University, 2021, 28; 27738059
Abstract (Summary)

This study compared several resampling methods for both the minority and majority classes to address the problematic issue of a highly skewed unbalanced data. In the initial experiments, deep learning models outperformed other machine learning approaches, these final experiments all used the same deep learning architecture of 9 layers (1 input, 7 hidden, 1 output). While Experiment 1 used the adam optimizer, 100 epochs, batch size 1000, 75%/25% train-test split, Experiment 2 used the nadam optimizer, 700 epochs, batch size 1000, 64%/16%/20% train-validation-test split. In addition, Experiment 2 found results on the original data produced by the techniques unlike Experiment 1. The purpose of Experiment 1 was to build basic models to initially test all the sampling methods. Experiment 2, which included a validation set, was structured to be a more rigorous examination.

These experiments demonstrate that the simple resampling methods, minority resampling and the minority and majority bootstrap, outperform the pre-built methods in python. These results suggest that majority of the prebuilt methods, as well as doing nothing (‘No balancing’) over-train on one of the two classes.

Indexing (document details)
Advisor: Zeng, Qing
School: The George Washington University
Department: Bioinformatics and Molecular Biochemistry
School Location: United States -- District of Columbia
Source: MAI 82/7(E), Masters Abstracts International
Subjects: Bioinformatics, Medicine, Artificial intelligence
Keywords: Bioinformatics, Deep neural network, Machine learning, Medicine, Neural network
Publication Number: 27738059
ISBN: 9798557078382
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