Zhong Mingyu, Rosander Bryan, Georgiopoulos Michael, Anagnostopoulos Georgios C, Mollaghasemi Mansooreh, Richie Samuel
School of EECS, University of Central Florida, Orlando, FL 32816, United States.
Neural Netw. 2007 Mar;20(2):245-59. doi: 10.1016/j.neunet.2006.11.008. Epub 2007 Jan 18.
Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data are of the noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this category proliferation. One of these modified Fuzzy ARTMAP architectures was the one proposed by Gomez-Sanchez, and his colleagues, referred to as Safe muARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe muARTMAP network parameters. Through a combination of these analytical arguments and experimentation we were able to identify good default parameter values for some of the Safe muARTMAP network parameters. This feat would allow one to save computations when a good performing Safe muARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe muARTMAP network for a variety of problems (simulated and real problems), and we compared it with other best performing ART networks, including other ART networks that claim to resolve the category proliferation problem in Fuzzy ARTMAP. These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe muARTMAP).
模糊ARTMAP（FAM）目前被认为是解决分类问题的主要神经网络架构之一。文献中广泛报道的模糊ARTMAP的局限性之一是类别增殖问题。也就是说，模糊ARTMAP有增加其网络规模的趋势，因为它面对的数据越来越多，特别是如果数据具有噪声和/或重叠的性质。为了解决这个问题，一些研究人员对模糊ARTMAP的训练阶段进行了设计修改，这些修改对减少这种类别增殖有有益的效果。其中一种修改后的模糊ARTMAP架构是戈麦斯-桑切斯及其同事提出的，称为安全μARTMAP。在本文中，我们提出了合理的分析论证，展示了我们应该如何选择安全μARTMAP网络的一些参数范围。通过这些分析论证和实验的结合，我们能够为安全μARTMAP网络的一些参数确定良好的默认参数值。当需要为新的分类问题识别性能良好的安全μARTMAP网络时，这一成果将使人们能够节省计算量。此外，我们进行了详尽的实验，以找到适用于各种问题（模拟问题和实际问题）的最佳安全μARTMAP网络，并将其与其他性能最佳的ART网络进行比较，包括其他声称能解决模糊ARTMAP中类别增殖问题的ART网络。这些实验结果使人们能够对多个ART网络（包括安全μARTMAP）进行成对比较时做出适当的陈述。
