Feature selection based on fuzzy-neighborhood relative decision entropy

Abstract

Abstract Feature selection facilitates pattern recognition, and fuzzy neighborhood rough sets provide an effective tool. By fuzzy neighborhood rough sets, we propose a heuristic feature selection algorithm based on fuzzy-neighborhood relative decision entropy, called AFNRDE. At first, the fuzzy-neighborhood relative decision entropy is proposed by granulation extension and information fusion, and it acquires uncertainty measurement, integration computing, and granulation monotonicity; then, the corresponding feature selection and heuristic reduction algorithm are constructed; finally, the measure monotonicity and algorithm validity are verified by numerical example and data experiment. AFNRDE promotes initial algorithm FSMRDE based on relative decision entropy to numerical data processing, and it also outperforms two usual methods FNRS and FNGRS for classification performance.