Feature Selection Method Based on Fuzzy Granularity Conditional Entropy and Improved Firefly Algorithm
A feature selection method,FS_FGCEIFA,was proposed based on fuzzy granularity conditional entropy and improved firefly algorithm(FA).By combining the knowledge granularity and λ-conditional entropy in fuzzy rough sets,a new information entropy model-fuzzy granularity conditional entropy was put forward;the fuzzy granularity conditional entropy was applied to FA,and a fitness function based on fuzzy granularity conditional entropy was proposed;the calculation strategy of particle inertia mass and universal gravity in the gravity search algorithm was adopted to adjust the brightness and attractiveness of fireflies in FA,and the adaptive step size factor based on gravity search was integrated into the position update of FA.Experiments on multiple UCI datasets and software defect prediction datasets show that FS_FGCEIFA could achieve better classification performance.
feature selectionfirefly algorithmgravity search algorithmfuzzy granularity conditional entropyfuzzy rough set
万方数据
维普
