Wu, Dalei; Wang, Yingfeng
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
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.
National Science Foundation (NSF) grant numbers 1924278, 1761839 and 1647175.
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.
Artificial intelligence; Machine learning; Neural networks (Computer science)
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Merrill, Jeremy, "Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition" (2021). Masters Theses and Doctoral Dissertations.