Multiple Kernel Probabilistic Clustering with Crow Lion Neural Network for Missing Data Imputation and Classification

Abstract

In pattern classification, one of the major problems is the missing data or data incompleteness, which is caused by different reasons. The amount of missing data varies depending on the applications. Missing data imputation is a technique used to handle the missing data problem by utilizing various techniques. The missing data imputation techniques replace the missing values by the estimated values. Numerous techniques are proposed for handling the missing data problem. Accordingly, this paper presents the missing data imputation and classification techniques for handling the missing values presented in the data set. Missing data imputation and classification is proposed by integrating the Multiple Kernel Probabilistic Clustering Algorithm (MKPCA) with Feed Crow Lion neural network (CLNN).