Example representation is a fundamental problem in machine learning. In particular, the decision on what features are extracted and selected to be included in the learning process significantly affects learning performance.
This work proposes a novel framework for feature representation based on feature properties and applies it to the domain of textual information extraction. Our framework enables knowledge on feature engineering and selection to be explicitly learned and applied. The application of this knowledge can improve learning performance within the domain from which it is learned and in other domains with similar representational bias.
We conducted several experiments comparing the performance of feature engineering and selection methods based on our framework with other approaches in the Information Extraction task. Results suggested that our approach performs either competitively or better than the best heuristic-based feature selection approach used. Moreover, our general framework can potentially be combined with other feature selection approaches to yield even better results.

PDF, PS

Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 85 pages.

@mastersthesis{kamolvilassatian:masters02, title={Property-Based Feature Engineering and Selection}, author={Noppadon Kamolvilassatian}, month={December}, school={Department of Computer Sciences, University of Texas at Austin}, address={Austin, TX}, pages={85 pages}, url="http://www.cs.utexas.edu/users/ai-lab?kamolvilassatian:masters02", year={2002} }