COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at www.proquest.com.

Questions? Please refer to this FAQ.

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
Commitee:
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
Source Type: DISSERTATION
Subjects: Bioinformatics, Medicine, Artificial intelligence
Keywords: Bioinformatics, Deep neural network, Machine learning, Medicine, Neural network
Publication Number: 27738059
ISBN: 9798557078382
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest