An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Neural network architecture selection: can function complexity help?
Neural Processing Letters
IEEE Transactions on Neural Networks
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Data discretization is an important processing step for several computational methods that work only with binary input data. In this work a method for discretize continuous data based on the use of the Extreme Learning Machine neural network architecture is developed and tested. The new method does not use data labels for performing the discretization process and thus is suitable for supervised and supervised data and also, as it is based on the Extreme Learning Machine, is very fast even for large input data sets. The efficiency of the new method is analyzed on several benchmark functions, testing the classification accuracy obtained with raw and discretized data, and also in comparison to results from the application of a state-of-the-art supervised discretization algorithm. The results indicate the suitability of the developed approach.