Comparison of Classifier-Specific Feature Selection Algorithms

摘要

The performance and speed of three classifier-specific feature selection algorithms, the sequential forward (backward) floating search (SFFS (SBFS)) algorithm, the ASFFS (ASBFS) algorithm (its adaptive version), and the genetic algorithm (GA) for large-scale problems are compared. The experimental results showed that 1) ASFFS (ASBFS) has better performance than does SFFS (SBFS) but requires much computation time, 2) much training in GA with a larger number of generations or with a larger population size, or both, is effective, 3) the performance of SFFS (SBFS) is comparable to that of GA with less training, and the performance of ASFFS (ASBFS) is comparable to that of GA with much training, but in terms of speed GA is better than ASFFS (ASBFS) for large-scale problems.