Incrementally updating a hybrid rule base based on empirical data |
| |
Authors: | Jim Prentzas Ioannis Hatzilygeroudis |
| |
Affiliation: | University of Patras, School of Engineering, Department of Computer Engineering and Informatics, 26500 Patras, Greece; Technological Educational Institute of Lamia, Department of Informatics and Computer Technology, 35100 Lamia, Greece E-mail: , |
| |
Abstract: | Abstract: Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. One way that the neurules can be produced is from training examples/patterns, extracted from empirical data. However, in certain application fields not all of the training examples are available a priori. A number of them become available over time. In those cases, updating the neurule base is necessary. In this paper, methods for updating a hybrid rule base, consisting of neurules, to reflect the availability of new training examples are presented. They can be considered as a type of incremental learning method that retains the entire induced hypothesis and all past training examples. The methods are efficient, since they require the least possible retraining effort and the number of neurules produced is kept as small as possible. Experimental results that prove the above argument are presented. |
| |
Keywords: | hybrid rule bases rule base maintenance incremental update/learning neuro-symbolic integration |
|
|