Switching Neural Networks: A New Connectionist Model for Classification
A new connectionist model, called Switching Neural Network (SNN), for the solution of classification problems is presented. SNN includes a first layer containing a particular kind of A/D converters, called latticizers, that suitably transform input vectors into binary strings. Then, the subsequent two layers of an SNN realize a positive Boolean function that solves in a lattice domain the original classification problem.
Every function realized by an SNN can be written in terms of intelligible rules. Training can be performed by adopting a proper method for positive Boolean function reconstruction, called Shadow Clustering (SC). Simulation results obtained on the StatLog benchmark show the good quality of the SNNs trained with SC.
KeywordsBinary String Connectionist Model Logical Product Boolean Lattice Multiclass Problem
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- 2.Muselli, M., Quarati, A.: Reconstructing positive Boolean functions with Shadow Clustering. In: Proceedings of the 17th European Conference on Circuit Theory and Design (ECCTD 2005), Cork, Ireland (August 2005)Google Scholar
- 4.Kohavi, R., Sahami, M.: Error-based and entropy-based discretization of continuous features. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 114–119 (1996)Google Scholar
- 9.Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1994)Google Scholar