越览（107）——精读硕士论文的1.5研究内容组织结构

摘要：This issue of tweets will introduce the research content organization structure of the master's thesis "Research on Multi-Attribut

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“越览（107）——精读硕士论文

《基于不完备信息系统的多属性模糊决策方法研究》

的1.5研究内容组织结构”。

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"Yuelan (107): Intensive reading of the master's thesis

"1.5 Research content organization structure of

multi-attribute fuzzy decision-making

methods based on incomplete

information systems"".

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍硕士论文《基于不完备信息系统的多属性模糊决策方法研究》的研究内容组织结构。

This issue of tweets will introduce the research content organization structure of the master's thesis "Research on Multi-Attribute Fuzzy Decision-making Method Based on Incomplete Information System" from three aspects: mind map, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

本文研究了基于信息系统和不完备信息系统的多属性决策方法，结合模糊模式识别评价法进行推进，主要内容包括以下三个方面：

This paper studies the multi-attribute decision-making method based on information systems and incomplete information systems, and combines it with fuzzy pattern recognition evaluation method to promote it. The main contents include the following three aspects:

1. 混合完备信息系统与模糊模式识别结合：本文提出了一种新型的混合完备信息系统，通过引入隶属度函数集合，将属性划分为自然属性和抽象属性，并进行标准化处理。决策者可以根据需求选择决策属性和条件属性，计算属性权重后确定标准集和方案集，从而在完备信息系统中得出最优方案。

1. Combination of hybrid complete information system and fuzzy pattern recognition: This paper proposes a new hybrid complete information system, which introduces a set of membership functions, divides attributes into natural attributes and abstract attributes, and performs standardized processing. Decision makers can select decision attributes and conditional attributes according to their needs, calculate the attribute weights, and determine the standard set and solution set, thereby obtaining the optimal solution in the complete information system.

2. 不完备信息系统的决策方法：本文针对不完备信息系统提出了一种新方法，引入隶属度函数集合，对缺失值采用模型补足法进行填充，并根据决策需求确定属性权重。通过综合考虑方案间的贴近度，最终选择出最优方案。

2. Decision-making method for incomplete information system: This paper proposes a new method for incomplete information system, introduces a set of membership functions, fills missing values with model completion method, and determines attribute weights according to decision requirements. By comprehensively considering the closeness between the options, the optimal option is finally selected.

3. 结合前景理论的决策方法：本文结合前景理论，增强决策者偏好的影响力，处理不完备信息系统中的多属性决策问题。通过标准化属性值、计算属性权重，并构建前景损失矩阵和前景价值矩阵，最终得出最优方案。

3. Decision-making method combined with prospect theory: This paper combines prospect theory to enhance the influence of decision makers' preferences and deal with multi-attribute decision-making problems in incomplete information systems. By standardizing attribute values, calculating attribute weights, and constructing prospect loss matrix and prospect value matrix, the optimal solution is finally obtained.

The organizational structure of this article is shown below:

四、知识补充（Knowledge supplement）

模型补足法是一种用于解决信息系统中缺失数据的技术，特别是在处理不完备信息系统时。这种方法通过填补缺失值来“补充”原始数据，从而使数据集变得更加完整，便于进行后续的分析和决策。

Model completion is a technique used to address missing data in information systems, especially when dealing with incomplete information systems. This method "supplements" the original data by filling in missing values, making the data set more complete and facilitating subsequent analysis and decision-making.

在多属性决策问题中，尤其是在不完备信息系统中，某些属性的值可能缺失或者不完全，这会影响决策结果的准确性。模型补足法的核心思想是利用已有的信息，推断缺失数据的值，使得决策分析不被缺失数据所干扰。通常，模型补足法会采用一些统计或数学模型来填补这些空白数据点，这些方法包括：

1. 基于已知数据推断缺失数据：通过分析数据中其他已知属性和缺失属性之间的关系，采用统计学模型（如回归分析、聚类分析、插值方法等）来估算缺失的值。

1. Infer missing data based on known data: By analyzing the relationship between other known attributes and missing attributes in the data, statistical models (such as regression analysis, cluster analysis, interpolation methods, etc.) are used to estimate missing values.

2. 多重插补法：使用多种插补方法或模型，根据不同的假设生成多个可能的补充方案，然后通过统计或计算方式综合这些不同的方案，以获得最优的补充结果。

2. Multiple imputation method: Use multiple imputation methods or models to generate multiple possible supplementation scenarios based on different assumptions, and then combine these different scenarios through statistical or computational methods to obtain the best supplementation results.

3. 属性重要性和信息完备度：在不完备信息系统中，可以根据属性的重要性或信息完备度来判断哪些属性更值得优先填充。这种方法通常结合权重评估，将重点放在信息最为重要的属性上进行补充。

3. Attribute importance and information completeness: In an incomplete information system, we can judge which attributes are more worthy of priority filling based on the importance of the attributes or the completeness of the information. This method is usually combined with weight evaluation to focus on the attributes with the most important information.

4. 模型扩展：对于某些复杂的决策模型，补足缺失数据可能需要对原有模型进行扩展或修改，加入更多的假设和条件，以更好地模拟缺失数据的填充过程。

4. Model expansion: For some complex decision-making models, filling missing data may require expanding or modifying the original model, adding more assumptions and conditions to better simulate the missing data filling process.

通过这些方法，模型补足法能够有效地解决不完备信息带来的决策困境，确保决策过程中数据的完整性和准确性。

Through these methods, model completion method can effectively solve the decision-making dilemma caused by incomplete information and ensure the integrity and accuracy of data in the decision-making process.

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