Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Merrill, Jeremy

Contributors dc:contributor

• Liang, Yu
• Wu, Dalei; Wang, Yingfeng
• College of Engineering and Computer Science

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

Licence

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

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/709

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1875