Open document
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General information |
| Author |
Khan, Naveed Kazim; Baig, Abdul Rauf; Iqbal, Muhammad Amjad |
| Published |
InTech Open Access Publisher, 2012
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| Abstract |
In this paper we present a new Discrete Particle
Swarm Optimization approach to induce rules from
discrete data. The proposed algorithm, called Oppositionbased
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. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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