Abstract—The critical dimension is the minimum number of features required for a learning machine to perform with “high” accuracy, which for a specific dataset is dependent upon the learning machine and the ranking algorithm. Discovering the critical dimension, if one exists for a dataset, can help to reduce the feature size while maintaining the learning machine’s performance. It is important to understand the influence of learning machines and ranking algorithms on critical dimension to reduce the feature size effectively. In this paper we experiment with three ranking algorithms and three learning machines on several datasets to study their combined effect on the critical dimension. Results show the ranking algorithm has greater influence on the critical dimension than the learning machine.
Index Terms—Critical dimension, feature selection, machine learning, ranking algorithm.
D. Suryakumar and A. H. Sung are with the Department of Computer
Science and Engineering, New Mexico Tech, Socorro, NM 87801, USA
Q. Liu is with the Department of Computer Science and Engineering, Sam Houston State University, Huntsville, Texas 77341, USA (e-mail: firstname.lastname@example.org)
Cite: Divya Suryakumar, Andrew H. Sung, and Qingzhong Liu, "Influence of Machine Learning vs. Ranking Algorithm on the Critical Dimension," International Journal of Future Computer and Communication vol. 2, no. 3 pp. 215-219, 2013.