# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 5 Performance Analysis and Discussion  
In the course of this investigation, a model for the selection of features based on a wrapper technique was adopted. The feature selection model used in previous studies is a sequential implementation of the wrapper technique, as illustrated in Figure 8. In the sequential technique, learning is conducted consecutively following feature subset  
Figure 6: Comparison of classification accuracies of proposed model with state-of-the-art feature selection algorithms.  
Figure 7: Comparison of time taken for classification with and without feature selection.  
generation, and if it does not pass the evaluation threshold, the feature subset generation is repeated; otherwise, the subset is chosen as the best feature set.  
In this work, a distributed feature selection approach was adopted, as depicted in Figure 9. In the proposed model, a feature subset is generated and the efficiency of the selected feature subset is evaluated in a distributed manner by the learning algorithm. If the generated subset satisfies the evaluation threshold, it is selected as the optimal feature set; otherwise, a new feature subset is generated and the evaluation process continues. The dataset is partitioned into different chunks as part of the learning process of the feature selection model, as shown in Figure 10.  
Figure 8: Existing sequential model of feature selection.  
Figure 10: Data partitioning in distributed model.  
Figure 9: Proposed distributed model for feature selection.  
The performance of the proposed model was compared with existing distributed feature selection models, namely VectorSlicer, RFormula, ChiSqSelectotr, UnivariateFeature Selector, and VarianceThreshold Selector. Table 2 demonstrates that, across the majority of datasets, the minimum number of features was acquired by using the proposed distributed FCM-based feature selection technique. The datasets for which the proposed algorithm selected the smallest number of features has been highlighted. For all but three datasets, the proposed model chose the smallest number of features. The effectiveness of the optimum feature set acquired using FCM-based feature selection was evaluated using five classification algorithms: Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron, and Multinomial Logistic Regression. After FCM-based feature selection, the datasets used for evaluation yielded accuracy values which are displayed in Table 3. The findings indicate that, compared to other classification methods, the Random Forest approach often yielded the highest accuracy scores for the considered datasets. The Random Forest method employs a majority agreement prediction technique, wherein a group of individual decision trees form an ensemble, thereby reducing the number of possible types of error and leading to higher accuracy of results. Hence, using the Random Forest algorithm, the performance of the proposed model was compared with existing feature selection algorithms. Figure 6 depicts the comparison of classification accuracies obtained when the proposed model was compared with existing methods. It can be noted that even though the number of features selected by the proposed model was not minimal in all the datasets, the figure depicts that the features selected were the optimal feature set, since the proposed model produced the best accuracy values as compared to other models.