Title

Self-Organizing Error-Driven (Soed) Artificial Neural Network (Ann) for Smarter Classification

Advisor

Smith, Brian K.

Committee Member

Medal, Hugh

Committee Member

Usher, John M.

Committee Member

Woody, Jonothan R.

Date of Degree

1-1-2018

Original embargo terms

Complete embargo for 3 years

Document Type

Dissertation - Open Access

Major

Industrial and Systems Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.

URI

https://hdl.handle.net/11668/17812

Comments

Artificial Neural Networks||Classification||Multi Layer Perceptron (MLP)||Self-Organizing Map (SOM)||Data-driven Decision-making

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