Big data classification using SpinalNet-Fuzzy-ResNeXt based on spark architecture with data mining approach

Abstract

In the modern networking topology, big data is highly essential for several domains like e-commerce, healthcare, and finance. Big data classification has offered effectual performance in several applications. Still, big data classification is highly difficult and the recognized classification approaches require a longer duration and numerous resources for executing the accessible data. For resolving such issues, the spark-based classification approach is required. In this work, the hybrid SpinalNet-Fuzzy-ResNeXt model called SFResNeXt is implemented to classify the big data. Here, the SpinalNet and ResNeXt are merged, where the layers are fused with the fuzzy concept. The initial process is the outlier detection. The Holoentrophy method is used to detect the outlier data, and it is removed. Moreover, duplicate detection is performed by fingerprinting approach to detect the repeated data. The, Association Rule Mining (ARM) method is employed for feature selection. The big data is classified by the SFResNeXt. Furthermore, the SFResNeXt-based big data classification offered the accuracy, sensitivity, and specificity of 0.905, 0.914, and 0.922 using the heart disease dataset.