A comparative analysis of text data classification accuracy and speed using neural networks, Bloom filter and naive Bayes

The object of research is the methods of fast classification for solving text data classification problems. The need for this study is due to the rapid growth of textual data, both in digital and printed forms. Thus, there is a need to process such data using software, since human resources are not able to process such an amount of data in full.

A large number of data classification approaches have been developed. The conducted research is based on the application of the following methods of classification of text data: Bloom filter, naive Bayesian classifier and neural networks to a set of text data in order to classify them into categories. Each method has both disadvantages and advantages. This paper will reflect the strengths and weaknesses of each method on a specific example. These algorithms were comparatively among themselves in terms of speed and efficiency, that is, the accuracy of determining the belonging of a text to a certain class of classification. The work of each method was considered on the same data sets with a change in the amount of training and test data, as well as with a change in the number of classification groups. The dataset used contains the following classes: world, business, sports, and science and technology. In real conditions of the classification of such data, the number of categories is much larger than that considered in the work, and may have subcategories in its composition.

In the course of this study, each method was analyzed using different parameter values to obtain the best result. Analyzing the results obtained, the best results for the classification of text data were obtained using a neural network.