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Incremental Continuous Ant Colony Optimization Technique for Support Vector Machine Model Selection Problem

机译：支持向量机模型选择问题的增量连续蚁群优化技术

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摘要

Ant Colony Optimization has been used to solve Support Vector Machine model selection problem. Ant Colony Optimization originally deals with discrete optimization problem. In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value. This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time. This study proposes an algorithm that can optimize Support Vector Machine parameters using Incremental Continuous Ant Colony Optimization without the need to discretize continuous value for support vector machine parameters. Seven datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithmin terms of classification accuracy. Promising results were obtained when compared to grid search technique.
机译：蚁群优化已用于解决支持向量机模型选择问题。蚁群优化最初处理离散优化问题。在应用蚁群优化来优化作为连续变量的支持向量机参数时，需要将连续值离散化为离散值。这种离散化过程将导致某些信息的丢失，从而影响分类的准确性和搜索时间。这项研究提出了一种算法，该算法可以使用增量连续蚁群优化来优化支持向量机参数，而无需离散化支持向量机参数的连续值。使用UCI的七个数据集，根据分类准确性评估了提出的混合算法的可信度。与网格搜索技术相比，获得了可喜的结果。

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