Big Data Classification Efficiency Based on Linear Discriminant Analysis

Abstract

The proliferation of online platforms recently has led to unprecedented increase in data generation; this has given rise to the concept of big data which characterizes data in terms of volume, velocity, variety, and veracity. One of the common multivariate statistical data analysis tools is linear discriminant analysis (LDA) which relies on the concept of obtaining the separation among groups through LDA. The prediction of the class of a given class of data points can be achieved through classification, a supervised learning technique but prior to a classification process, a classification model must first be built using classification algorithms. Several classification algorithms are available for prediction tasks. LDA is commonly used for the reduction of the dimensionality of datasets. In this article, the use of LDA to improve the classification performance of different classification model was presented.

Keywords

• Spark
• Parallel Processing
• Dimensionality Reduction