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Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

Automatic text classification and clustering are still very challenging computational problems to the information retrieval (IR) communities both in academic and industrial contexts. Currently, a great effort of work on IR, one can find in the literature, is focused on classification and clustering of generic content of text documents. However, there are many other important applications to which little attention has hitherto been paid, which are as well very difficult to deal with. One example of these applications is the classification of companies based on the descriptions of their economic activities, also called mission statements, which represent the business context of the companies’ activities, in other words, the business economic activities from free text description by the company’s founders. The categorization of companies according to their economic activities constitute a very important step towards building tools for obtaining correct information for performing statistical analysis of the economic activities within a city or country. With this goal, the Brazilian government is creating a centralized digital library with the business economic activity descriptions of all companies in the country. This library will serve the three government levels: Federal; the 27 States; and more than 5.000 Brazilian counties. We estimate that the data related to nearly 1.5 million companies will have to be processed every year (DNRC, 2007) into more than 1.000 possible different activities. It is important to highlight that the large number of possible categories makes this problem particularly complex when compared with others presented in the literature (Jain et al., 1999; Sebastiani, 2002). In this paper, we proposed a slightly modified version of the standard structure of the probabilistic neural network (PNN) (Specht, 1990) so that we could deal with the multi-label problem faced in this work. We compared the PNN performance trained by a canonical Particle Swarm Optimization (PSO) and a Bare Bones Particle Swarm Optimization (BBPSO). Our results show that, in the categorization of free text descriptions of economic activities, the PNN trained by BBPSO got slightly better results than the PNN trained by PSO.