Analytical method for selection an informative set of features with limited resources in the pattern recognition problem

Abstract

Feature selection is one of the most important issues in Data Mining and Pattern Recognition. Correctly selected features or a set of features in the final report determines the success of further work, in particular, the solution of the classification and forecasting problem. This work is devoted to the development and study of an analytical method for determining informative attribute sets (IAS) taking into account the resource for criteria based on the use of the scattering measure of classified objects. The areas of existence of the solution are determined. Statements and properties are proved for the Fisher type informativeness criterion, using which the proposed analytical method for determining IAS guarantees the optimality of results in the sense of maximizing the selected functional. The relevance of choosing this type of informativeness criterion is substantiated. The universality of the method with respect to the type of features is shown. An algorithm for implementing this method is presented. In addition, the paper discussed the dynamics of the growth of information in the world, problems associated with big data, as well as problems and tasks of data preprocessing. The relevance of reducing the dimension of the attribute space for the implementation of data processing and visualization without unnecessary difficulties is substantiated. The disadvantages of existing methods and algorithms for choosing an informative set of attributes are shown.

A Step toward Next-Generation Advancements in the Internet of Things Technologies

“…Generally, the data are known as big data or not—it mainly depends upon the volume. A recent survey [ 37 ] presents data growth dynamics and prediction trends, as shown in Figure 5 . In this figure, we examine that the size of data has increased in the last 16 years.…”

“…In this figure, we examine that the size of data has increased in the last 16 years. It will dramatically increase in the coming years and is forecasted to reach 175 zettabytes in 2024~2025 [ 37 ]. The global big data market is forecasted to grow to 175 billion U.S. dollars by 2025, more than double its expected market size in 2018.…”

“…This rapid growth in volume and the size of data are due to the use of modern technologies such as cloud computing and edge computing, etc. [ 37 ]. The term “velocity” refers to the speed at which data are gathered in the network [ 12 , 38 ].…”