دستهبندی دادههای بزرگ با استفاده از دستهبندیکنندههای SVM
Abstract
The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.
Introduction
Big Data is a term for data sets that are so large and/or complex that traditional data processing technologies are inadequate. They require technologies that can be used to store and process the exponentially increasing data sets which contain structured, semi structured and unstructured data. Volume, variety and velocity are three defining characteristics of Big Data. Volume refers to the huge amount of data, variety refers to the number of data types and velocity refers to the speed of data processing. The problems of the Big Data management result from the expansion of all three characteristics. The Big Data does not consist of only numbers and strings but also geospatial data, audio, video, web data, social files, etc. obtained from various sources such as sensors, mobile phones, cameras and so on