Performance evaluation of feature selection methods for aircraft hard landing incident Prediction

Abstract

AbstractThis poster aims to find out a quick and accurate way for feature selection in hard landing prediction. To make the performance evaluation effective, a general evaluation framework is designed. Then seven classic feature selection methods are carried out by applying four classification algorithms, nine comparison metrics on a real QAR dataset with a total of 3,040 instances. At last, TOPSIS is employed for overall performance evaluation. Results indicate that the data‐driven category is preferable in feature selection of hard landing prediction. Moreover, Gradient Boosting Decision Tree combining with K‐Nearest Neighbor classifier on a balanced training dataset outperforms among 56 possible combination models.