Feature selection based on fuzzy-neighborhood relative decision entropy

Feature selection facilitates pattern recognition, and fuzzy neighborhood rough sets provide an effec-tive 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 uncer-tainty measurement, integration computing, and granulation monotonicity; then, the corresponding fea-ture 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 algo-rithm FSMRDE based on relative decision entropy to numerical data processing, and it also outperforms two usual methods FNRS and FNGRS for classification performance. (c) 2021 Elsevier B.V. All rights reserved.