Machine Learning Methods for Multi-criteria Decision Making

多标准决策的机器学习方法

• 批准号: 250377-2012
• 负责人: Yao, JingTao
• 金额: $ 1.02万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570909
• 关键词: Machine; Learning; Methods; Multi; criteria

Multi-criteria decision making remains an active and challenging research topic. The proposed research program aims to utilize machine learning methods, such as rough sets and granular computing, in rule induction for complex decision making. In particular, I will examine three-way decision making, especially when multi-criteria and multi-agent are involved. I will first study issues related to three-way decision making. With rough sets, rules are extracted and classified into three categories called the positive, negative and the boundary rules. However, in many cases, we may not be able to make necessary and timely decisions because there are too many uncertain decision rules. In addition, we should consider both the accuracy of prediction rules and the consequences of applying these rules in real applications. A possible solution is to lower expectations for accuracy by converting uncertain decision rules to certain decision rules. As accuracy is sacrificed, unacceptable consequences may occur in some cases. Game theory will be used to resolve such dilemmas by locating balanced positions that meet the needs of generalization and accuracy. Mechanisms to find equilibriums or thresholds will be examined. I will then examine real applications where multiple agents or multiple measures are involved in decision making. In these situations, multiple positive, negative and boundary regions result from using three-way decision methods. A balanced or consensus decision needs to be made. Traditionally, such decisions were made using majority, committee, or unanimous decisions. A combination of granular computing and game theory will be used to reach an intelligent consensus. Possible applications of this method are feature selection for text categorization and Web-based decision support systems. In summary, I will study the possibility of building hybrid intelligent systems that assist humans to make informative and wise decisions on complex problems involving multiple criteria and multiple agents. It is expected that this will broaden our knowledge of decision support mechanisms.

多目标决策仍然是一个活跃而富有挑战性的研究课题。提出的研究计划旨在利用机器学习方法，如粗糙集和粒计算，进行复杂决策的规则归纳。特别是，我将研究三方决策，特别是当涉及多标准和多主体时。 我将首先研究与三方决策有关的问题。利用粗糙集对规则进行提取，将规则分为正规则、负规则和边界规则三类。然而，在许多情况下，我们可能无法做出必要和及时的决策，因为有太多不确定的决策规则。此外，我们应该同时考虑预测规则的准确性和在实际应用中应用这些规则的后果。一种可能的解决方案是通过将不确定的决策规则转换为确定的决策规则来降低对准确性的期望。由于牺牲了准确性，在某些情况下可能会出现不可接受的后果。博弈论将被用来解决这样的两难境地，通过寻找满足普遍性和准确性需要的平衡头寸。我们将研究寻找均衡或阈值的机制。 然后，我将研究涉及多个代理或多个衡量标准的决策制定的实际应用。在这些情况下，使用三向决策方法会产生多个正、负和边界区域。需要做出一个平衡的或一致的决定。传统上，这样的决定是通过多数、委员会或一致决定作出的。粒计算和博弈论的结合将被用来达成智能共识。该方法可能应用于文本分类的特征选择和基于Web的决策支持系统。 总之，我将研究建立混合智能系统的可能性，以帮助人类在涉及多个标准和多个代理的复杂问题上做出信息丰富和明智的决策。预计这将扩大我们对决策支持机制的了解。