Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
Tim J. Menzies.
Modern day machine learning is not without its shortcomings. To start with, the heuristic accuracy, which is the standard assessment criteria for machine learning, is not always the best heuristic to gauge the performance of machine learners. Also machine learners many times produce theories that are unintelligible by people and must be assessed as automated classifiers through machines. Theses theories are either too large or not properly formatted for human interpretation. Furthermore, our studies have identified that most of the data sets we have encountered are satiated with worthless data that actually leads to the degradation of the accuracy of machine learners. Therefore, simpler learning is more optimal. This necessitates a simpler classifier that is not confused with highly correlated data. Lastly, existing machine learners are not sensitive to domains. That is, they are not tunable to search for theories that are most beneficial to specific domains.
Ammar, Kareem, "Multi-heuristic theory assessment with iterative selection" (2004). Graduate Theses, Dissertations, and Problem Reports. 1524.