Committee Chair

Liang, Yu

Committee Member

Wu, Dalei; Wang, Yingfeng

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

To classify the data-set featured with a large number of heavily imbalanced classes, this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have arbitrary number of children. Compared with the Binary Huffman-Tree Neural Network (BHTNN), EHTNN may have smaller tree height, involve fewer component neural networks, and demonstrate more flexibility on handling data imbalance. Using a 16-class exponentially imbalanced audio data-set as the benchmark, the proposed EHTNN was strictly assessed based on the comparisons with alternative methods such as BHTNN and single-layer CNN. The experimental results demonstrated promising results about EHTNN in terms of Gini index, Entropy value, and the accuracy derived from hierarchical multiclass confusion matrix.

Acknowledgments

National Science Foundation (NSF) grant numbers 1924278, 1761839 and 1647175.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2021

Subject

Artificial intelligence; Machine learning; Neural networks (Computer science)

Keyword

Artificial Intelligence; Classification; Convolutional Neural Network; Huffman; Machine Learning

Document Type

Masters theses

DCMI Type

Text

Extent

x, 30 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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