Author Description

The effect of missing values on data classification is studied. A comparative analysis of data classification accuracy in different scenarios is presented. Several search techniques are considered in the study for feature selection and are applied to pre-process the dataset. The predictive performances of popular classifiers are compared quantitatively. The dataset is drawn from a breast cancer detection-decision context available at UCI machine learning repository. After analysing the experimental results, the work establishes the general concept of improved classification accuracy using missing values replacement.