Feature Selection Techniques for Big Data Analytics

Abstract

Big data applications have tremendously increased due to technological developments. However, processing such a large amount of data is challenging for machine learning algorithms and computing resources. This study aims to analyze a large amount of data with classical machine learning. The influence of different random sampling techniques on the model performance is investigated by combining the feature selection techniques and machine learning classifiers. The experiments used two feature selection techniques: random subset and random projection. Two machine learning classifiers were also used: Naïve Bayes and Bayesian Network. This study aims to maximize the model performance by reducing the data dimensionality. In the experiments, 400 runs were performed by reducing the data dimensionality of a video dataset that was more than 40 GB. The results show that the overall performance fluctuates between 70% accuracy to 74% for using sampled and non-sample (all the data), a slight difference in performance compared to the non-sampled dataset. With the overall view of the results, the best performance among all combinations of experiments is recorded for combination 3, where the random subset technique and the Bayesian network classifier were used. Except for the round where 10% of the dataset was used, combination 1 has the best performance among all combinations.