NDLI: Opposition-Based Discrete PSO Using Natural Encoding for Classification Rule Discovery

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Opposition-Based Discrete PSO Using Natural Encoding for Classification Rule Discovery

Content Provider	SAGE Publishing
Author	Khan, Naveed Kazim Baig, Abdul Rauf Iqbal, Muhammad Amjad
Copyright Year	2012
Abstract	In this paper we present a new Discrete Particle Swarm Optimization approach to induce rules from discrete data. The proposed algorithm, called Opposition-based Natural Discrete PSO (ONDPSO), initializes its population by taking into account the discrete nature of the data. Particles are encoded using a Natural Encoding scheme. Each member of the population updates its position iteratively on the basis of a newly designed position update rule. Opposition-based learning is implemented in the optimization process. The encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes within the rule's antecedent to be a disjunction of the values of those attributes. The performance of the proposed algorithm is evaluated against seven different datasets using a tenfold testing scheme. The achieved median accuracy is compared against various evolutionary and non-evolutionary classification techniques. The algorithm produces promising results by creating highly accurate and precise rules for each dataset.
Related Links	https://journals.sagepub.com/doi/pdf/10.5772/51728?download=true
ISSN	17298806
Issue Number	5
Volume Number	9
Journal	International Journal of Advanced Robotic Systems (ARX)
e-ISSN	17298814
DOI	10.5772/51728
Language	English
Publisher	Sage Publications UK
Publisher Date	2012-01-01
Publisher Place	London
Access Restriction	Open
Rights Holder	© 2012 Khan et al.; licensee InTech.
Subject Keyword	classification swarm intelligence rule list sequential covering algorithm OBL Particle Swarm Optimization (PSO) Discrete PSO
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Science Applications Software

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