ارائه روشی ترکیبی برای افزایش دقت پیش‌بینی در کاهش داده با استفاده از مدل مجموعه راف و هوش تجمعی

Designing a system with an emphasis on minimal human intervention helps users to explore information quickly. Adverting to methods of analyzing large data is compulsory as well. Hence, utilizing power of the data mining process to identify patterns and models become more essential from aspect of relationship between the various elements in the database and discover hidden knowledge. Therefore, Rough set theory can be used as a tool to explore data dependencies and reducing features outlined in a data set. The main purpose of the rough theory is to obtain approximate concepts of acquired data. This theory is a powerful mathematical tool for arguing in ambiguous and indeterminate terms that provides methods for remove and reduce unrelated or excessive knowledge information on the data sets. This process of data reduction is based on the main task of the system, and without losing the basic data of the data sets. Rough set theory can play a very effective role to support decision-making systems, but in some cases, with increasing data volumes, there are inconsistent or collisional results which using swarm intelligence-based methods can choose the best of the contradictory, effectless or dummy data. This will bring interesting, unexpected and valuable structures from within a wide range of data. Since the ant colony optimization compares all the exploratory paths generated by each ant and the best route is selected from the existing paths, so considering the improvement of the selecting the main features and improving the theory of the Rough set, paths are not eliminated from the possible paths. In this research, the combination of the ant colony optimization and rough set theory have been used to find the subset of the main features and to delete the inappropriate information with the loss of the minimum information. This research will improve the features reduction technique employment Rough set theory and ant colony optimization. The gist of this research is removing useless information with minimal information loss. The results on petroleum prices data evaluation demonstrate that the hybrid method is more efficient than recent methods.