A self-organizing computing network for decision-making in data sets with a diversity of data types
A self-organizing computing network based on concepts of fuzzy conditions, beliefs, probabilities, and neural networks is proposed for decision-making in intelligent systems which are required to handle data sets with a diversity of data types. A sense-function with a sense-range and fuzzy edges is defined as a transfer function for connections from the input layer to the hidden layer in the network. By generating hidden cells and adjusting the parameters of the sense-functions, the network self-organizes and adapts to a training set. Computing cells in the input layer are designed as data converters so that the network can deal with both symbolic data and numeric data. Hidden computing cells in the network can be explained via fuzzy rules in a similar manner to those in fuzzy neural networks. The values in the output layer can be explained as a belief distribution over a decision space. The final decision is made by means of the winner-take-all rule. The approach was applied to a series of the benchmark data sets with a diversity of data types and comparative results obtained. Based on these results, the suitability of a range of data types for processing by different intelligent techniques was analyzed, and the results show that the proposed approach is better than other approaches for decision-making in information systems with mixed data types.
information technology and systems decision support machine learning fuzzy sets