Missing Data Estimation Using Ant-Lion Optimizer Algorithm

Abstract

Ant-lion optimizer (ALO) algorithm is also a population-based meta-heuristic algorithm capable of finding approximate solutions to complex optimization problems. In this chapter, we present another new framework for missing data imputation in the high-dimensional dataset. A deep autoencoder is used in conjunction with the ALO algorithm (DL-ALO). The performance of the proposed technique is experimentally tested and compared against other existing methods of a similar nature using an off-line handwritten digits image recognition dataset. The results obtained are in line with those from previous chapters, further emphasizing the effectiveness and applicability of a deep learning framework in the domain being considered. Although the model portrays slightly longer execution times, which are a worthy trade-off when accuracy is of importance in real-world applications, it is important to further consider such frameworks.