Use of fuzzy if-then rules for pattern classification

D. Mandal, H. Tanaka
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引用次数: 1

Abstract

An efficient fuzzy partitioning method of a feature space for pattern classification problems is proposed in this article. A feature space is initially decomposed into some overlapping subspaces depending on the relative positions of the pattern classes found in the training samples. To reflect the pattern classes by the generated subspaces, a few fuzzy if-then rules are then obtained in terms of a relational matrix. The relational matrix is utilized in the modified compositional rule of inference in order to recognize an unknown pattern. The proposed system is capable of handling incomplete and other imprecise information both in the learning and processing phases. The effectiveness of the system is demonstrated on two real life problems. The proposed system is capable of reflecting the nonoverlapping, overlapping and no-class regions of the feature space by providing output decisions in terms of single, multiple and null choices. The multivalued outputs are found to be superior than existing classical and fuzzy approaches.<>
使用模糊if-then规则进行模式分类
针对模式分类问题,提出了一种有效的特征空间模糊划分方法。根据在训练样本中发现的模式类的相对位置,特征空间最初被分解成一些重叠的子空间。为了通过生成的子空间反映模式类,然后根据关系矩阵获得一些模糊的if-then规则。在改进的组合推理规则中利用关系矩阵来识别未知模式。所提出的系统能够在学习和处理阶段处理不完整和其他不精确的信息。通过两个实际问题验证了该系统的有效性。该系统通过提供单选择、多选择和空选择的输出决策,能够反映特征空间的非重叠、重叠和无类区域。发现多值输出优于现有的经典方法和模糊方法。
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