Adaptive resonance theory (ART) networks, including developmental resonance network (DRN), basically use a vigilance parameter as a hyperparameter to determine whether a current input can belong to any existing categories or not. The problem here is that the clustering quality of those networks is sensitive to the vigilance parameter so that the users are required to fine-tune the parameter delicately beforehand. Another problem is that those networks only deal with a hyperrectangular decision boundary, which means they cannot learn categories of arbitrary shape. In addition, the order of data processing is a critical factor to categorize clusters correctly because each category can expand its boundary into the areas of other categories erroneously. To deal with these problems, we propose an advanced version of DRN, Adaptive DRN (A-DRN), which learns the vigilance parameters assigned for individual category nodes as well as category weights. The proposed A-DRN combines close categories to construct a cluster that contains the categories identifying a cluster boundary of arbitrary shape. Our A-DRN also employs a sliding window. The sliding window buffers sequential data points to presume the data distribution roughly, which helps our network to have a robust and consistent performance to a random order of input data. Through the experiments, we empirically demonstrate the effectiveness of A-DRN in both synthetic and real-world benchmark data sets.

中文翻译：

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自适应发育共振网络


自适应共振理论（ART）网络，包括发育共振网络（DRN），基本上使用警戒参数作为超参数来确定当前输入是否可以属于任何现有类别。这里的问题是这些网络的聚类质量对警戒参数很敏感，因此需要用户事先对参数进行微调。另一个问题是这些网络仅处理超矩形决策边界，这意味着它们无法学习任意形状的类别。此外，数据处理的顺序是正确对簇进行分类的关键因素，因为每个类别都可能错误地将其边界扩展到其他类别的区域。为了解决这些问题，我们提出了 DRN 的高级版本，自适应 DRN（A-DRN），它学习为各个类别节点分配的警戒参数以及类别权重。所提出的 A-DRN 结合了相近的类别来构建一个集群，其中包含标识任意形状的集群边界的类别。我们的 A-DRN 还采用了滑动窗口。滑动窗口缓冲顺序数据点以粗略地推测数据分布，这有助于我们的网络对输入数据的随机顺序具有鲁棒且一致的性能。通过实验，我们凭经验证明了 A-DRN 在合成和现实世界基准数据集中的有效性。