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Classification based on Gaussian-kernel Support Vector Machine with Adaptive Fuzzy Inference System

机译：基于高斯核支持向量机的自适应模糊推理系统分类

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摘要

In this paper, we propose a new classification approach which combines the advantages of both Gaussian-kernel Support Vector Machine and Adaptive Fuzzy Inference System. Instead of generating a large number of candidate rules as in fuzzy classification, the proposed method adopts the decision trees to generate rules directly from training data. Decision trees provide architecture to generate fuzzy IF-THEN rules from the training data where the fuzzy parameters of the rules would be optimized using Genetic Algorithm. The Gaussian-kernel SVM will be used in the classification phase using the parameters obtained from Particle Swarm Optimization. Experimental results of the proposed approach has proved significantly better accuracy than other state-of-the-art classification methods by testing it on benchmark UCI datasets.
机译：在本文中，我们提出了一种新的分类方法，该方法结合了高斯核支持向量机和自适应模糊推理系统的优点。所提方法不是像模糊分类那样生成大量候选规则，而是采用决策树直接从训练数据中生成规则。决策树提供了一种从训练数据生成模糊IF-THEN规则的体系结构，其中可以使用遗传算法对规则的模糊参数进行优化。高斯核支持向量机将在分类阶段使用从粒子群优化获得的参数进行分类。通过在基准UCI数据集上进行测试，该方法的实验结果已证明其准确性比其他最新分类方法明显更好。

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