# Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
## 3 Methods
### Non-Dominated Binary Grasshopper Optimization Algorithm (NSBGOA)
It was found that all the multi-objective methods gave a better balance of objectives than the single-objective methods. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers. It also resulted in better mean objective values in most case. This study showed that the performance of the multi-objective method is affected by the base classifier chosen. As such, the implementation of multi-objective methods should carefully consider the base classifier used for evaluation. Conceptualization, N.S., S.O. and N.K. ; methodology, N.S. and S.O. ; formal analysis, validation, visualization, and writing original draft, N.S. ; review and editing, S.O., A.K. and N.K. ; supervision, N.K. ; project administration and funding acquisition, A.K. and N.K. All authors have read and agreed to the published version of the manuscript. This work was supported by a JSPS KAKENHI Grant (Grant Number JP19H04100). This was waived because data were anonymized by the data provider before they were provided to authors for research purposes. Restrictions apply to the availability of these data. Data were obtained from Agribuddy Ltd. and are available with the permission of Agribuddy Ltd. The authors would like to thank Agribuddy Ltd. for their kind assistance in providing data. The authors declare no conflict of interest. ## References

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