Class Center-Based Firefly Algorithm for Handling Missing Data

Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.