Designing an efficient fuzzy classifier using an intelligent genetic algorithm

2002 ◽  
Author(s):  
Shinn-Ying Ho ◽  
Tai-Kang Chen ◽  
Shinn-Jang Ho
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Classifier ◽  
Intelligent Genetic Algorithm

scholarly journals Using Genetic Algorithm to Minimize False Alarms in Insider Threats Detection of Information Misuse in Windows Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Maaz Bin Ahmad ◽  
Adeel Akram ◽  
M. Asif ◽  
Saeed Ur-Rehman
Keyword(s):  
Genetic Algorithm ◽  
Vital Role ◽  
False Alarms ◽  
Detection Problem ◽  
Specific Nature ◽  
Fuzzy Classifier ◽  
Insider Threats ◽  
Mathematical Evaluation ◽  
Operational Processes ◽  
Detection Mechanisms

Insider threats detection problem has always been one of the most difficult challenges for organizations and research community. Effective behavioral categorization of users plays a vital role for the success of any detection mechanisms. It also helps to reduce false alarms in case of insider threats. In order to achieve this, a fuzzy classifier has been implemented along with genetic algorithm (GA) to enhance the efficiency of a fuzzy classifier. It also enhances the functionality of all other modules to achieve better results in terms of false alarms. A scenario driven approach along with mathematical evaluation verifies the effectiveness of the modified framework. It has been tested for the enterprises having critical nature of business. Other organizations can adopt it in accordance with their specific nature of business, need, and operational processes. The results prove that accurate classification and detection of users were achieved by adopting the modified framework which in turn minimizes false alarms.

LEARNING DECISION REGIONS BASED ON ADAPTIVE ELLIPSOIDS

2014 ◽  
Vol 22 (01) ◽  
pp. 41-73 ◽  
Author(s):  
LEEHTER YAO ◽  
KUEI-SUNG WENG
Keyword(s):  
Genetic Algorithm ◽  
Effective Index ◽  
Cluster Validity ◽  
Fuzzy Classifier ◽  
Learning Capability ◽  
Point Distribution ◽  
Underlying Distribution ◽  
Data Points ◽  
Approximate Decision ◽  
Validity Measure

A fuzzy classifier using multiple ellipsoids to approximate decision regions for classification is designed in this paper. To learn the sizes and orientations of ellipsoids, an algorithm called evolutionary ellipsoidal classification algorithm (EECA) that integrates the genetic algorithm (GA) with the Gustafson-Kessel algorithm (GKA) is proposed. Within EECA the GA is employed to learn the size of every ellipsoid. With the size of every ellipsoid encoded and intelligently estimated in the GA chromosome, GKA is utilized to learn the corresponding ellipsoid. GKA is able to adapt the distance norm to the underlying distribution of the prototype data points for an assigned ellipsoid size. A process called directed initialization is proposed to improve EECA's learning efficiency. Because EECA learns the data point distribution in every cluster by adjusting an ellipsoid with suitable size and orientation, the information contained in the ellipsoid is further utilized to improve the cluster validity. A cluster validity measure based on the ratio of summation for each intra-cluster scatter with respect to the inter-cluster separation is defined in this paper. The proposed cluster validity measure takes advantage of EECA's learning capability and serves as an effective index for determining the adequate number of ellipsoids required for classification.

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