Extração de Regras de Redes Neurais por meio do Algoritmo RX Modificado: Um Exemplo de Aplicação em Modelagem de Dados Meteorológicos.

The main challenge in using supervised neural networks in data mining applications means to get explicit knowledge from these models. For this purpose, an algorithm for rule extraction from artificial neural networks, based on the hidden units activation values, is developed. This algorithm, denominated Modified RX, was already evaluated in two benchmarks - Iris Plants database and Pima Indians Diabetes database - and the results were published previously. This work deals with the application of this algorithm to a dataset containing 10,000 examples of meteorological observations collected at the International Airport of Rio de Janeiro. Each example is represented by 38 attribute values and one associated class - wet fog or dry fog. Following the data preparation tasks - data representation, data selection and correlation analysis - a neural network is trained to model wet and dry fog conditions, and then the Modified RX Algorithm is used for rule extraction. The results obtained from the rule set provided by the algorithm are compared to those obtained from a classification tree.