The dendritic cell algorithm (DCA) is a classification algorithm based on the behavior of natural dendritic cells (DCs). In literature, DCA has given good classification results. However, DCA was known to be sensitive to the order of the instance classes. To solve this limitation, a fuzzy DCA version was developed stating that the cause of such sensitivity is related to the DCA crisp classification task (hypothesis 1). In this paper, we hypothesize that there is a second possible cause of such DCA sensitivity which is related to the possible existence of noisy instances presented in the DCA signal data set (hypothesis 2). Thus, we aim, first of all, to test the trueness of the latter hypothesis, and second, we aim to develop an overall hybrid DCA taking both hypotheses into consideration. Based on hypothesis 1, our new DCA focuses on smoothing the crisp classification task using fuzzy set theory. Based on hypothesis 2, a data set cleaning technique is used to guarantee the quality of the DCA signal data set. Results show that our proposed hybrid fuzzy maintained algorithm succeeds in obtaining results of interest.

中文翻译：

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一种基于树突细胞的混合模糊维护分类方法

树突细胞算法 (DCA) 是一种基于自然树突细胞 (DC) 行为的分类算法。在文献中，DCA给出了很好的分类结果。然而，众所周知，DCA 对实例类的顺序很敏感。为了解决这个限制，开发了一个模糊 DCA 版本，说明这种敏感性的原因与 DCA 清晰分类任务有关（假设 1）。在本文中，我们假设这种 DCA 敏感性存在第二个可能的原因，这与 DCA 信号数据集中可能存在的噪声实例有关（假设 2）。因此，我们的目标首先是测试后一个假设的真实性，其次，我们的目标是开发一个综合考虑这两个假设的整体混合 DCA。基于假设 1，我们新的 DCA 专注于使用模糊集理论来平滑清晰的分类任务。基于假设2，使用数据集清洗技术来保证DCA信号数据集的质量。结果表明，我们提出的混合模糊维护算法成功地获得了感兴趣的结果。