Corrigendum to “An inverse classification framework with limited budget and maximum number of perturbed samples” (Expert Systems With Applications (2023) 212, (S0957417422017791), (10.1016/j.eswa.2022.118761))

The authors regret that the name of the diabetes dataset (Section 5.3) is incorrect. It should be ‘Pima Indians Diabetes Database’ instead of ‘Diabetes 130-US hospitals Dataset.’ The name is wrong, but all experimental results and discussions are the same. In the article, the beginning of Section 5.3 should read as follows: We experiment with another public dataset that describes clinical information of diabetes patients, Pima Indians Diabetes Database from the UCI Machine Learning Repository (Dua & Graff, 2017). In this study, we use the data and the same preprocessing provided by Li (2018). It has 768 samples without missing values and eight input features corresponding to the health state for diabetes patient predictions. We adopt a … The authors would like to apologize for any inconvenience caused.