Encoding symbolic features in simple decision systems over ontological graphs for PSO and neural network based classifiers

摘要

In the paper, we present the idea of encoding symbolic features appearing in simple decision systems over ontological graphs for building classifiers based on Particle Swarm Optimization (PSO) as well as Neural Networks. Simple decision systems over ontological graphs refer to a general trend in computations proposed by Zadeh and called "computing with words". In case of such decision systems, we deal with attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver us some additional knowledge which can be useful in classification processes. Symbolic data, in our approach in the form of concepts from ontologies, require special treatment to be used in classifiers based on searching for the numerical mapping functions between the known inputs and the corresponding known outputs.