Incorporation of fuzziness in ID3 and generation of network architecture
This article first describes a fuzzy version of ID3, called fuzzy ID3 by incorporating fuzziness at input, output and node levels. A fuzziness measure is computed at each node, in terms of class membership, to rake care of the uncertainty arising from overlapping regions. The measure is such that in the crisp (nonoverlapping) case, the algorithm boils down to the classical ID3, A confidence factor is estimated at the nodes for both making a decision and determining the rule base for network mapping. In the second part, we deal with a scheme of designing a fuzzy knowledge-based network by encoding an MLP with the rules generated using fuzzy ID3, whereby the network topology is automatically determined. The frequency of samples (representative of a rule) and the confidence factors of unresolved/ambiguous nodes are taken into consideration during mapping. The effectiveness of the system, in terms of recognition scores and speed of convergence, is demonstrated on two real life data sets.
fuzzy id3 rule generation classification neural tree neuro-fuzzy paradigm soft computing