Chaotic Biogeography Based Optimization Using Deep Stacked Auto Encoder for Big Data Classification

Big data's expansion has increased the need for efficient data classification methods. An optimization method inspired by nature called Chaotic Biogeography Based Optimization (CBBO) has showed promise in the solution of challenging optimization issues. But high-dimensional large data classification issues can be challenging for CBBO. To solve this problem, a Deep Stacked Auto Encoder (DSAE) is added to CBBO to produce a revolutionary big data categorization algorithm called CBBO-DSAE. On several benchmark datasets, the proposed method is assessed and contrasted with other cutting-edge algorithms. The experimental findings show that CBBO-DSAE performs better than other existing algorithms in managing big data categorization issues.