Using a Probabilistic Neural Network for a Large Multi-label Problem

Oliveira, E.Ciarelli, P.M.Souza, A.Badue, C.

The automation of the categorization of economic activities from business descriptions in free text format is a huge challenge for the Brazilian governmental administration in the present day. When this problem is tackled by humans, the subjectivity on their classification brings another problem: different human classifiers can give different results when working on a set of the same business descriptions. This can cause a serious distortion on the information for the planning and taxation of the governmental administrations on the three levels: County, State and Federal. Furthermore, the number of possible categories considered is very large, more than 1000 in the Brazilian scenario. The large number of categories makes the problem even harder to be solved, as this is also a multi-labeled problem. In this work we compared the multi-label lazy learning technique, ML-kNN, to our probabilistic neural network approach. Our implementation overcome the ML-kNN algorithm in four metrics typically used in the literature for multi-label categorization problems.

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