International Journal of Security and Networks
| Hi-index | 0.00 |
Generating decision rules is one of the most important data mining areas which “Rough Set Data Analysis(RSDA)” can address. Generally, for the same expression, the shorter the rules are, the more effectively the system performances. Considering of this, this paper provides a new heuristic algorithm named “Short First Extraction (SFE)” based on the classical rough set theory, for rules generation. A standard named “All Attribute in Rules’ Length(AARL)” to compare the rules’ ability is also provided. Our experiments is based on the datasets provided by UCI machine learning repository, such as iris datasets, new-thyroid dataset and yellow-small(balloons) dataset. The experiments’ results indicate that “SFE” always has better performance than JohnsonReducer, genetic reducer and Holte’s 1R reducer: it always generates less rules and has lower “AARL” than its competitors. Our “SFE” algorithm also has another property which may be useful: the rules generated by “SFE” is a covering but not a partition of the information system, and it may lead us to a new direction of rules generating research.