NOVEL APPROACH FOR BIG DATA CLASSIFICATION BASED ON HYBRID PARALLEL DIMENSIONALITY REDUCTION USING SPARK CLUSTER

The big data concept has elicited studies on how to accurately and efficiently extract valuable information from a huge dataset. The major problem during big data mining is data dimensionality, which is due to the large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers; it also results in the wasting of time due to the presence of several redundant features in a dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL that is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. An evaluation of the proposed HP-PL on the CICIDS2017 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required ?1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (a total of 21 cores). For the comparative algorithm, more than two hours was required to achieve the same feat. In the proposed system, Hadoop's distributed file system (HDFS) was used to achieve distributed storage, while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL.