Applying the JBOS reduction method for relevant knowledge extraction

摘要

This work presents results from an experiment used to assess the JBOS (junction based on objects similarity) reduction method. Two reductions were made of a formal context about patients having symptoms in a tuberculosis data base. The first reduction used the knowledge expressed in the original formal context and the second used the knowledge expressed in expert rules. The assessment was made, in the first case, by comparison of the performances of the sets of extracted rules (stem bases) before and after the reduction, and in the second case, by comparison of the performances of the set of extracted rules after reduction with that of the expert rules. The performance in the first case was exactly the same as before reduction. In the second case the performance even improved, showing that the weighting process, besides incorporating the expert knowledge, resulted in rules well adjusted to the knowledge expressed in the original formal context. So, both reductions resulted in rule sets absolutely consistent with the original ones. The expert rules, FCA rules and both set of rules obtained after reduction were used also to classify patients of a validation set. In this case, the results have shown that the performance was the same before and after reduction. Therefore, it was shown that by means of an appropriate attributes weight assignment it is possible, by the JBOS method, to achieve a suitable level of performance in a specific task after reduction.