جستجوی مقالات مرتبط با کلیدواژه "rough set theory" در نشریات گروه "برق"

In recent years, rough set theory has become one of the powerful solutions in solving the problem of artificial intelligence and data mining. But the classic version of the Raff set theory is not very suitable for discussing feature reduction in imperfect information systems. An incomplete information system refers to data tables that do not have a value in some attribute directories. Feature selection based on fuzzy rough sets is an effective approach to select the best subset of features. Fuzzy set theory and rough set theory are two distinct but complementary theories that deal with uncertainty in data. The salient features of both theories are within the scope of fuzzy rough tuning theory. This hybrid theory is useful as a potential tool for data mining, especially for feature selection. However, there are relatively few studies on incomplete data with time intervals. The purpose of this paper is to present a fuzzy set approach based on overcoming information systems with incomplete value. Since feature reduction is an NP-hard problem, a fast and effective approximation algorithm is required.. In this paper A new optimization approach known as water cycle algorithm has been used to solve this problem. The presented method was tested on the known UCI dataset. The results of the experiments show that the fuzzy rough and the proposed algorithm gave appropriate results, which is reasonable.

Increasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer league competition algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low algorithm’s parameters were the motivation of considering the algorithm in the current research. The proposed ideas to modify the algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art algorithms. This comparison shows that the competitive advantages of the proposed algorithm over the investigated algorithms.

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.