A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

Abstract

Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation.

Saman Khamesian

Graduate Research Associate

I am a Ph.D. researcher at Arizona State University, specializing in Artificial Intelligence with a focus on health applications. As a Graduate Research Assistant in the EMIL Lab under Dr. Hassan Ghasemzadeh, I work on developing advanced machine learning solutions for Type 1 diabetes management, including personalized glucose forecasting and automated insulin delivery systems.