Missing Data Imputation With Granular Semantics and AI-driven Pipeline
  for Bankruptcy Prediction
Debarati Chakraborty Ravi Ranjan
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 space, in the granular space. The
granules are formed around every missing entry, considering a few of the highly
correlated features and most reliable closest observations to preserve the
relevance and reliability, the context, of the database against the missing
entries. An intergranular prediction is then carried out for the imputation
within those contextual granules. That is, the contextual granules enable a
small relevant fraction of the huge database to be used for imputation and
overcome the need to access the entire database repetitively for each missing
value. This method is then implemented and tested for the prediction of
bankruptcy with the Polish Bankruptcy dataset. It provides an efficient
solution for big and high-dimensional datasets even with large imputation
rates. Then an AI-driven pipeline for bankruptcy prediction has been designed
using the proposed granular semantic-based data filling method followed by the
solutions to the issues like high dimensional dataset and high class-imbalance
in the dataset. The rest of the pipeline consists of feature selection with the
random forest for reducing dimensionality, data balancing with SMOTE, and
prediction with six different popular classifiers including deep NN. All
methods defined here have been experimentally verified with suitable
comparative studies and proven to be effective on all the data sets captured
over the five years.