摘要
Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.
