Multi-heuristic theory assessment with iterative selection

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Lane Department of Computer Science and Electrical Engineering

Year dc:date.available

2004

Author dc:creator

• Ammar, Kareem

Contributors dc:contributor

• Tim J. Menzies.

• Computer science

Identifier

https://researchrepository.wvu.edu/etd/1524

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2527