  Biomedical data is filled with continuous real values; these values in the
feature set tend to create problems like underfitting, the curse of
dimensionality and increase in misclassification rate because of higher
variance. In response, pre-processing techniques on dataset minimizes the side
effects and have shown success in maintaining the adequate accuracy. Feature
selection and discretization are the two necessary preprocessing steps that
were effectively employed to handle the data redundancies in the biomedical
data. However, in the previous works, the absence of unified effort by
integrating feature selection and discretization together in solving the data
redundancy problem leads to the disjoint and fragmented field. This paper
proposes a novel multi-objective based dimensionality reduction framework,
which incorporates both discretization and feature reduction as an ensemble
model for performing feature selection and discretization. Selection of optimal
features and the categorization of discretized and non-discretized features
from the feature subset is governed by the multi-objective genetic algorithm
(NSGA-II). The two objective, minimizing the error rate during the feature
selection and maximizing the information gain while discretization is
considered as fitness criteria.
