LAUSR

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle component analysis, linear discriminate analysis, and linear square regression are used. Since the length of feature vector tends to be high, the choice of the optimal features is complex task. Hence, the proposed model utilizes the optimal feature selection technology referred as Lion-based Firefly (L-FF) algorithm to select the optimal features. The main objective of this article is projected on minimizing the correlation between the selected features. It results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called neural network (NN) is adopted, which effectively classify the data in an effective manner with the selected features. Furthermore, the proposed L-FF+NN model is compared with the traditional methods and proves the effectiveness over other methods. Experimental analysis shows that the proposed L-FF+NN model is 92%, 28%, 87%, 82%, and 78% superior to the state-of-art models such as GA+NN, FF+NN, PSO+NN, ABC+NN, and LA+NN, respectively.