An effective classification approach for big data with parallel generalized Hebbian algorithm

Abstract

Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.

Improved artificial neural networks based whale optimization algorithm

“…through the addition of a local search parameter, contraction factors and inertia weight. Therefore, the model used to train the ANN model was based on 10 known datasets sourced from the UCI ML repository for performance improvement [7]. The classification accuracy of the WOA-trained ANN was compared with that of the backpropagation-trained ANN, and the results demonstrated the superior performance of the WOA-trained ANN.…”