Pal Nikhil R, Saha Seemanti
Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta 700108, India.
IEEE Trans Syst Man Cybern B Cybern. 2008 Dec;38(6):1626-38. doi: 10.1109/TSMCB.2008.2006367.
One of the main attractions of a fuzzy rule-based system is its interpretability which is hindered severely with an increase in the dimensionality of the data. For high-dimensional data, the identification of fuzzy rules also possesses a big challenge. Feature selection methods often ignore the subtle nonlinear interaction that the features and the learning system can have. To address this problem of structure identification, we propose an integrated method that can find the bad features simultaneously when finding the rules from data for Takagi-Sugeno-type fuzzy systems. It is an integrated learning mechanism that can take into account the nonlinear interactions that may be present between features and between features and fuzzy rule-based systems. Hence, it can pick up a small set of useful features and generate useful rules for the problem at hand. Such an approach is computationally very attractive because it is not iterative in nature like the forward or backward selection approaches. The effectiveness of the proposed approach is demonstrated on four function-approximation-type well-studied problems.
基于模糊规则的系统的主要吸引力之一在于其可解释性，但随着数据维度的增加，这一特性会受到严重阻碍。对于高维数据，模糊规则的识别也面临巨大挑战。特征选择方法往往忽略了特征与学习系统之间可能存在的微妙非线性交互。为了解决结构识别问题，我们提出一种集成方法，该方法在为高木-关野（Takagi-Sugeno）型模糊系统从数据中寻找规则时，能够同时找出不良特征。这是一种集成学习机制，它可以考虑特征之间以及特征与基于模糊规则的系统之间可能存在的非线性交互。因此，它能够挑选出一小部分有用特征，并为手头的问题生成有用的规则。这种方法在计算上非常有吸引力，因为它不像前向或后向选择方法那样本质上是迭代的。所提出方法的有效性在四个经过充分研究的函数逼近型问题上得到了验证。
