论文标题
具有集成特征选择和规则提取的自适应神经模糊系统，用于高维分类问题
An Adaptive Neuro-Fuzzy System with Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems
论文作者
论文摘要
模糊或神经模糊系统的主要局限性是他们未能处理高维数据集。这主要是由于使用T-Norm，特别是产品或最小值（或柔和的版本）。因此，几乎没有任何尺寸超过一百多个数据集的工作。在这里，我们提出了一个神经模糊框架，该框架可以处理具有尺寸甚至超过7000的数据集！在这种情况下，我们提出了一个自适应软敏度（ADA-SOFTMIN），该软敏度有效地克服了``数字底流量''和````假假）在处理高维问题的同时出现的``伪造最小值''。我们称其为自适应的高加壁kang（adatsk）模糊系统。然后，我们将Adatsk系统装备以集成方式执行特征选择和规则提取。在这种情况下，只有在两个连续的学习阶段才能确定有用的特征和规则，从而介绍和嵌入了新的门功能。与传统的模糊规则基础不同，我们设计了一个增强的模糊规则基础（EN-FRB），该规则基础（EN-FRB）保持了足够的规则，但并未将规则的数量呈指数增长，而这些规则的数量通常是模糊神经网络通常发生的。集成的特征选择和规则提取ADATSK（FSRE-ADATSK）系统由三个顺序阶段组成：（i）特征选择，（ii）规则提取和（iii）微调。在19个数据集中证明了FSRE-ADATSK的有效性，其中5个在2000多个维度中，其中两个尺寸大于7000。这可能是第一次实现模糊系统用于涉及7000多个输入功能的分类。
A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-dimensional datasets. This happens primarily due to the use of T-norm, particularly, product or minimum (or a softer version of it). Thus, there are hardly any work dealing with datasets with dimensions more than hundred or so. Here, we propose a neuro-fuzzy framework that can handle datasets with dimensions even more than 7000! In this context, we propose an adaptive softmin (Ada-softmin) which effectively overcomes the drawbacks of ``numeric underflow" and ``fake minimum" that arise for existing fuzzy systems while dealing with high-dimensional problems. We call it an Adaptive Takagi-Sugeno-Kang (AdaTSK) fuzzy system. We then equip the AdaTSK system to perform feature selection and rule extraction in an integrated manner. In this context, a novel gate function is introduced and embedded only in the consequent parts, which can determine the useful features and rules, in two successive phases of learning. Unlike conventional fuzzy rule bases, we design an enhanced fuzzy rule base (En-FRB), which maintains adequate rules but does not grow the number of rules exponentially with dimension that typically happens for fuzzy neural networks. The integrated Feature Selection and Rule Extraction AdaTSK (FSRE-AdaTSK) system consists of three sequential phases: (i) feature selection, (ii) rule extraction, and (iii) fine tuning. The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with dimension greater than 7000. This may be the first time fuzzy systems are realized for classification involving more than 7000 input features.
