Enabling data security in data using vertical split with parallel feature selection using meta heuristic algorithms

Abstract

SummaryBig data is the emerging trend in modern science that deals with datasets larger and more complex that cannot be dealt by the traditional data processing techniques. This seems to be the core of current technology and business. In practice, many criteria should be considered in the implementation of this technique. The way of the search space for finding potential subsets of features and prediction performance of classifiers are major important issue. To solve this issue, feature selection methods are introduced in the recent work. In the feature selection algorithm, Non‐deterministic Polynomial (NP) Hard, and searching the space has been becomes more difficult task. To solve this problem, this work provides a new approach toward feature selection based on Vertical Split Group FireFly (VSGFF) algorithm. FF algorithm gets its inspiration from social aspects of real fireflies. At the same time, VSGFF is proposed with the principle of multiple clusters to avoid privacy problem. Finally, Naïve Bayes (NB), K Nearest Neighbor (KNN), and Multi‐Layer Perceptron Neural Network (MLPNN) classification algorithms are proposed for big data classification. Experimental outcomes depicts that proposed technique improves classification accuracy by 4% compared to traditional vertical split firefly algorithm.