Hybrid Kernel Fuzzy Clustering with Feed Lion Neural Network for Missing Data Imputation and Classification

Abstract

A common issue in many practical applications associated with pattern classification is data incomplete or missing data due to various reasons that differ based on the applications. Missing data imputation is a promising approach used to handle this issue, which fills the missing attributes with estimated values following several techniques. This paper proposes a new strategy for data imputation and classification using a hybrid prediction model that combines Hybrid Kernel Fuzzy Clustering (HKFC) and Feed Lion Neural Network (FLNN). FLNN considers the missingattributes as the class attributes to predict the missing data, which will also be computed using HKFC and finally, the combined effect of these two approaches provides the missing data. FLNN is designed for the classification by modifying