ارائه روشي تركيبي براي افزايش دقت پيش‌بيني در كاهش داده با استفاده از مدل مجموعه راف و هوش تجمعي

A New Hybrid Method to Increase the Prediction in Data Reduced Using Rough Set and Swarm Intelligence Model

نياز به طراحي سامانه‌هايي كه قادر به اكتشاف سريع اطلاعات مورد علاقه كاربران با تاكيد بر كمينه مداخله انساني باشند از يك سو و روي‌آوردن به روش‌هاي تحليل متناسب با حجم داده‌هاي حجيم ازسوي ديگر، در دنياي امروزي به‌خوبي احساس مي‌شود. از‌اين‌رو بهره‌گيري از قدرت فرآيند داده‌كاوي جهت شناسايي الگوها و مدل‌ها و نيز ارتباط عناصر مختلف در پايگاه داده جهت كشف دانش نهفته در داده‌ها روز‌به‌روز ضروري‌تر مي‌شود. از سوي ديگر تئوري مجموعه راف را مي‌توان به‌عنوان يك ابزار براي كشف وابستگي داده‌ها و كاهش خصيصه‌هاي موجود در يك مجموعه داده، تنها با استفاده از داده‌ها و بدون نياز به اطلاعات اضافي برشمرد. در اين پژوهش جهت بهبود روند انتخاب ويژگي‌هاي اصلي و بهبود تئوري مجموعه راف، از تركيب الگوريتم مورچگان و تئوري مجموعه راف جهت يافتن زيرمجموعه ويژگي‌هاي اصلي و حذف اطلاعات غير مفيد با از دست رفتن كمينه اطلاعات استفاده شده است. نتايج حاصل از اين تركيب در ارزيابي داده‌هاي قيمت نفت نشان مي دهد كه تركيب الگوريتم مورچگان و تئوري مجموعه راف در انتخاب ويژگي هاي مفيد و بهينه، عملكرد مناسب‌تري نسبت به مدل هاي اخير دارد.

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.