Evaluating the impact of multivariate imputation by MICE in feature selection

Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets. Aim: Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values. Results: We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets. Conclusions: Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.

Automated summary

Handling missing values is crucial in preprocessing data for Machine Learning, and using multivariate imputation can reduce bias in the feature selection process and lead to better results.