Data Mining by Discrete PSO Using Natural Encoding

In this paper we have presented a new Discrete Particle Swarm Optimization approach to induce rules from the discrete data. Particles are encoded using Natural Encoding scheme. Encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attribute...

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Bibliographic Details
Published in2010 5th International Conference on Future Information Technology pp. 1 - 6
Main Authors Khan, Naveed Kazim, Iqbal, Muhammad Amjad, Baig, A Rauf
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Classification algorithms
Data mining
Decision making
Encoding
Error analysis
Evolutionary computation
Genetics
Induction generators
Particle swarm optimization
System testing
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Summary:In this paper we have presented a new Discrete Particle Swarm Optimization approach to induce rules from the discrete data. Particles are encoded using Natural Encoding scheme. Encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes in the rule antecedent to be disjunction of values of those attributes. The performance of the proposed algorithm is evaluated against six different datasets using tenfold testing scheme. Achieved error rate has been compared against various evolutionary and non-evolutionary classification techniques. The algorithm produces promising results by creating highly accurate rules for each dataset.
ISBN:1424469481
9781424469482
ISSN:2159-7006
DOI:10.1109/FUTURETECH.2010.5482723