Slime Mold Optimization with Relational Graph Convolutional Network for Big Data Classification on Apache Spark Environment

Abstract

Lately, Big Data (BD) classification has become an active research area in different fields namely finance, healthcare, e-commerce, and so on. Feature Selection (FS) is a crucial task for text classification challenges. Text FS aims to characterize documents using the most relevant feature. This method might reduce the dataset size and maximize the efficiency of the machine learning method. Various researcher workers focus on elaborating effective FS techniques. But most of the presented techniques are assessed for smaller datasets and validated by a single machine. As textual data dimensionality becomes high, conventional FS methodologies should be parallelized and improved to manage textual big datasets. This article develops a Slime Mold Optimization based FS with Optimal Relational Graph Convolutional Network (SMOFS-ORGCN) for BD Classification in Apache Spark Environment. The presented SMOFS-ORGCN model mainly focuses on the classification of BD accurately and rapidly. To handle BD, the SMOFS-ORGCN model uses an Apache Spark environment. In the SMOFS-ORGCN model, the SMOFS technique gets executed for reducing the profanity of dimensionality and to improve classification accuracy. In this article, the RGCN technique is employed for BD classification. In addition, Grey Wolf Optimizer (GWO) technique is utilized as a hyperparameter optimizer of the RGCN technique to enhance the classification achievement. To exhibit the better achievement of the SMOFS-ORGCN technique, a far-reaching experiments were conducted. The comparison results reported enhanced outputs of the SMOFS-ORGCN technique over current models.