Topic
Rough set
About: Rough set is a research topic. Over the lifetime, 14683 publications have been published within this topic receiving 256804 citations.
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01 Jan 2010
TL;DR: In this article, the authors outline conceptualization and implementation of an intelligent system capable of extracting knowledge from databases, which is based on rough sets, a theory of sets that is related to many computer science domains such as artificial intelligence, database, data mining, expert systems, decision support system and geographic information system.
Abstract: Knowledge engineering refers to the building, maintaining and development of knowledge-based systems. It has a great deal in common with software engineering and is related to many computer science domains such as artificial intelligence, database, data mining, expert systems, decision support system and geographic information system. The main motto of rough sets is: “Let the data speak for themselves”. Rough sets are based theory of sets. Main applications of rough sets theory are attribute reduction, rule generation and prediction. This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases.
2 citations
Journal Article•
TL;DR: A new algorithm for computing core is proposed after analyzing the indiscernibility relation and radix sorting, and its time complexity is O(|C||U)|, which is proved more efficient and suitable for large data sets.
Abstract: The efficiency of attribute reduction is a key issue in rough set and other soft computing theories.In order to enhance it,a new algorithm for computing core is proposed after analyzing the indiscernibility relation and radix sorting,and its time complexity is O(|C||U)|.Furthermore,a quick reduction algorithm which uses improved attribute significance as heuristic information is presented,the time complexity is O(|C|2|U)|.Finally,through some experiments on the data sets in UCI machine learning repository, the algorithm is proved more efficient and suitable for large data sets.
2 citations
01 Dec 2016
TL;DR: The improved combined cross entropy algorithm of attribute reduction is proved to be feasible and can achieve the smallest reduction of data sets and has a high precision and classification accuracy.
Abstract: To acquire the optimal attribute reduction, a novel method was proposed on the basis of cross-entropy algorithm. The method divides the samples generated by the cross entropy into the elite samples and the common samples, and generates a good sample set for the variation of the elite samples and the update of the common samples. The model of attribute reduction of rough set is solved by using the improved combined cross entropy algorithm. Applied to the UCI data concentration tested and compared with the other algorithms, the improved combined cross entropy algorithm of attribute reduction is proved to be feasible. What's more, the algorithm can achieve the smallest reduction of data sets and has a high precision and classification accuracy.
2 citations
01 Apr 2008
2 citations
09 Oct 2006
TL;DR: In this paper, a rough set based anomaly detection method was proposed to identify masqueraders in cellular mobile networks. But the performance of the scheme was evaluated by a simulation and the results showed that the roughness membership function was ineffective in detecting masquerade attacks.
Abstract: The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers – masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation.
2 citations