Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
An empirical investigation of factors affecting ubiquitous computing use and U-business value
International Journal of Information Management: The Journal for Information Professionals
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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.