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In this paper, a new approach is proposed for feature reduction using a GA-Rough hybrid approach on Bio-medical data. The given set of bio-medical data is pre-processed with the min-max normalization method. Then the subsequent evaluation on each feature with respect to the output class is carried out utilizing the information gain-based approach using the entropy-based discretization. Features with...
In rough set literature, methods for inducing minimal rules from a decision table have been proposed. When the decision attribute is ordinal, inducing rules about upward and downward unions of decision classes is advantageous in the simplicity of the obtained rules. However, because of independent applications of the rule induction method, inclusion relations among upward/downward unions in conclusion...
In order to avoid the network intrusion, the network intrusion detection method is studied and developed. In the paper, a hybrid method of rough set and support vector machine are adopted to network intrusion detection. The detection model includes the data reduction by rough set and network intrusion recognition by support vector machine. The 680 cases are collected to study the superiority of the...
In rough set literatures, methods for inducing minimal rules from a given decision table have been proposed. When the decision attribute is ordinal, inducing rules about upward and downward unions of decision classes is advantageous in the simplicity of obtained rules. However, because of independent applications of the rule induction method, inclusion relations among upward/downward unions in conclusion...
Most classification studies are done by using all the objects data. It is expected to classify objects by using some subsets data in the total data. A rough set based reduct is a minimal subset of features, which has almost the same discernible power as the entire conditional features. Here, we propose a greedy algorithm to compute a set of rough set reducts which is followed by the k-nearest neighbor...
The main function of IDS (intrusion detection system) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine)...
The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relearning...
In order to compress or reduce redundant features in customers churn, the rough set theory is introduced and a feature reduction algorithm based on the rough set is proposed. An example in customers churn is given to validate the algorithm. The results show that when the customers churn classification result is almost invariable, the main features which are more important to the churn classification...
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