Multi-objective Feature Selection with Missing Data in Classification
  Feature selection (FS) is an important research topic in machine learning.
Usually, FS is modelled as a+ bi-objective optimization problem whose
objectives are: 1) classification accuracy; 2) number of features. One of the
main issues in real-world applications is missing data. Databases with missing
data are likely to be unreliable. Thus, FS performed on a data set missing some
data is also unreliable. In order to directly control this issue plaguing the
field, we propose in this study a novel modelling of FS: we include reliability
as the third objective of the problem. In order to address the modified
problem, we propose the application of the non-dominated sorting genetic
algorithm-III (NSGA-III). We selected six incomplete data sets from the
University of California Irvine (UCI) machine learning repository. We used the
mean imputation method to deal with the missing data. In the experiments,
k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature
subsets. Experimental results show that the proposed three-objective model
coupled with NSGA-III efficiently addresses the FS problem for the six data
sets included in this study.
