In current decade, Social Spider Optimization (SSO) has become popular among researchers due to its ability to represent and handle very high and complex dimensional solution space. Like the other nature inspired algorithms, it also takes inspiration from nature. It mimics the cooperative behavior of social spiders in the forests. Unlike the other nature inspired algorithms, its agents have gender property due to which the algorithm maintains the balance between exploration and exploitation. Recently, a few researchers have employed SSO for clustering data. In this article, we propose a new classification algorithm called All Prototypes Social Spider Optimization for Data Classification(APSSODC) in which each spider has the prototypes of all data instances of the dataset. As the dimensionality of solution space in APSSODC is very high and equal to the product of degree and cardinality of the dataset, we propose another algorithm called Single Prototype Social Spider Optimization for Data Classification (SPSSODC) that reduces the dimensionality of the solution space. It considers each spider as a single prototype of a data instance present in the dataset. We found that SPSSODC outperforms the existing algorithms including APSSODC with respect to classification accuracy.
CITATION STYLE
Thalamala, R., Janet, B., & Reddy, A. V. (2019). A novel data classifier using social spider optimization. International Journal of Innovative Technology and Exploring Engineering, 8(8), 1756–1773.
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