Robust Feature Selection Algorithms

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. This paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency.

• Type: Conference
• Year: 1993
• Where: ICTAI
• Authors: Haleh Vafaie, Kenneth DeJong
• Keywords: Artificial Intelligence, Feature Selection Algorithms, Genetic Algorithms, ICTAI 1993, Machine Learning