NDLI: Bio-inspired algorithm for classification association rules

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2012 8th International Conference on Informatics and Systems (INFOS)

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Table of contents

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2010 The 7th International Conference on Informatics and Systems (INFOS)

Bio-inspired algorithm for classification association rules

Content Provider	IEEE Xplore Digital Library
Author	Soliman, O.S. Adly, A.
Copyright Year	2012
Description	Author affiliation: Cairo University, Faculty of Computers and Information, 5 Ahmed Zewal Street, Orman, Giza, Cairo, Egypt (Soliman, O.S.; Adly, A.)
Abstract	Associative classification (AC) has shown a great dominance over many classification techniques. Associative classification uses association rule mining for rules discovery process to identify data class labels. Associative classification also integrates the rule discovery and classification process to build the classifier that supports in decision making process. The main advantages of the associative classification approaches is to discover high quality association rules in a very large space of candidate rules and integrate these rules with the classification process efficiently. Artificial Immune Systems (AIS) have emerged during the last decade, Artificial immune systems can be defined as a computational system that is inspired by theoretical immunology, observed immune principles and mechanisms. The AIS uses the population-based search model of evolutionary computation algorithms that it is regarded as a suitable way for dealing with complex search space. This paper proposes an ambitious algorithm based on Quantum-Inspired Immune system (QIS) for building an efficient classifier by searching association rules to find the best subset of rules for all possible association rules. The proposed algorithm employees a mutation operator with a quantum-based rotation gate to control and maintain diversity, and guides the search process. The performance of proposed algorithm is evaluated using benchmark datasets. The experimental results showed that the proposed algorithm is preformed well with large search space and has higher accuracy, and control algorithm diversity.
File Size	2393164
File Format	PDF
ISBN	9781467308281
e-ISBN	9789774035067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-14
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Cairo University
Subject Keyword	Algorithm design and analysis Accuracy Itemsets Classification algorithms Association rules Optimization Immune system
Content Type	Text
Resource Type	Article

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