Missing Data Imputation With Granular Semantics and AI-driven Pipeline for Bankruptcy Prediction

arXiv:2404.00013v1 Announce Type: new
Abstract: This work focuses on designing a pipeline for the prediction of bankruptcy. The presence of missing values, high dimensional data, and highly class-imbalance databases are the major challenges in the said task. A new method for missing data imputation with granular semantics has been introduced here. The merits of granular computing have been explored here to define this method. The missing values have been predicted using the feature semantics and reliable observations in a low-dimensional …

abstract arxiv challenges class class-imbalance cs.ai cs.lg data databases designing imputation major missing values pipeline prediction q-fin.st said semantics stat.ap type values work