Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Computer Science

Committee Chair

H. S. Ranganath

Committee Member

Ramazan Aygun

Committee Member

Huaming Zhang

Committee Member

Tathagata Mukherjee

Committee Member

Marc L. Rusey


Machine learning., Computer algorithms., Electronic data processing.


Many machine learning applications require a high degree of confidence in the correctness of the classification results. Ideally, no decision made by the classification model should be a misclassification. However, no classifier can guarantee to provide a correct classification for all data items. Therefore, it is reasonable to allow the classification model to classify a data item only when the certainty level is high and reject to make a decision if the certainty level is not high. In this dissertation, a high confidence data classification method is developed. The method helps the classification model avoid classifying data items that are difficult to classify, which allows reducing the classification error to as low as zero and increasing the confidence in the classification results. In addition, using the new method, new neural network and decision tree classifiers and their training algorithms are developed. These new classifiers provide an extra output to indicate that the data item cannot be classified into a predefined class and is a reject. The claim that the method empowers the classification model to learn to determine the set of difficult to classify data samples automatically during the training process is validated through the classification experiments of diverse datasets. All experiments have evaluated the classifiers and algorithms presented in this dissertation on achieving the objective of the new method. The research presented in this dissertation has brought us one step closer to achieving near zero classification error for increasing our confidence in the correctness of the prediction made by the classification model.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.