This article introduced a new multi-attribute information classification method by employing intuitionistic fuzzy set (IFS) approach. The proposed method was referred as four-way intuitionistic decision space (4WIDS). In the 4WIDS, IFS theory was used to model the inherent uncertainty of multi-attribute information. For generating more precise level of decision-rules, granular computing (GrC) approach was employed. The proposed 4WIDS method was appropriate for the classification of the multi-attribute information into four different regions as positive IFS, negative IFS, uncertain IFS and gray IFS regions. Detail methodology of the 4WIDS was explained by presenting its representation in a precise way. This study also presented various definitions, properties and theorems in the support of the 4WIDS method. The 4WIDS was applied in benchmark datasets that included Pima Indians diabetes, Thyroid disease, Fisher’s Iris and Spambase datasets. Experimental results including statistical analysis indicated that the proposed 4WIDS outperformed existing classification methods, such as Naive Bayes, Decision tree, PART, J48, logistic model trees (LMT), rough set (RS), gray multi-granulation rough set (GMGRS) and multi-granulation fuzzy rough set (MGFRS).

中文翻译：

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直觉模糊集的多属性信息分类与决策方法

本文介绍了一种采用直觉模糊集（IFS）方法的多属性信息分类新方法。提出的方法称为四向直觉决策空间（4WIDS）。在4WIDS中，IFS理论被用来对多属性信息的固有不确定性进行建模。为了生成更精确级别的决策规则，采用了粒度计算（GrC）方法。提出的4WIDS方法适用于将多属性信息分为四个不同的区域，即正IFS，负IFS，不确定IFS和灰色IFS区域。4WIDS的详细方法论以精确的方式进行了解释。这项研究还提出了支持4WIDS方法的各种定义，性质和定理。4WIDS应用于基准数据集，包括比马印第安人糖尿病，甲状腺疾病，Fisher's Iris和Spambase数据集。包括统计分析在内的实验结果表明，提出的4WIDS优于现有的分类方法，例如朴素贝叶斯，决策树，PART，J48，逻辑模型树（LMT），粗糙集（RS），灰色多粒度粗糙集（GMGRS）和多粒度模糊粗糙集（MGFRS）。