RMAIN: Association rules maintenance without reruns through data

Association rules are well recognised as a data mining tool for analysis of transactional data, currently going far beyond the early basket-based applications. A wide spectrum of methods for mining associations have been proposed up to date, including batch and incremental approaches. Most of the accurate incremental methods minimise, but do not completely eliminate reruns through processed data. In this paper we propose a new approximate algorithm RMAIN for incremental maintenance of association rules, which works repeatedly on subsequent portions of new transactions. After a portion has been analysed, the new rules are combined with the old ones, so that no reruns through the processed transactions are performed in the future. The resulting set of rules is kept similar to the one that would be achieved in a batch manner. Unlike other incremental methods, RMAIN is fully separated from a rule mining algorithm and this independence makes it highly general and flexible. Moreover, it operates on rules in their final form, ready for decision support, and not on intermediate representation (frequent itemsets), which requires further processing. These features make the RMAIN algorithm well suited for rule maintenance within knowledge bases of autonomous systems with strongly bounded resources and time for decision making. We evaluated the algorithm on synthetic and real datasets, achieving promising results with respect to either performance or quality of output rules.     

基于Rough集的规则学习研究

Rough Sets方法是一种处理不确定或模糊知识的重要工具,本文在对Rough Sets理论进行深入研究的基础上,提出了一种基于Rough Sets的自增量学习算法,该算法利用简化的差异矩阵和置信度,能较好地进行确定性规则和非确定性规则的学习。     

Survey of Mahjong game AI construction methods北大核心CSCD

Mahjong and its different variants have complex rules. Therefore, building a high-level Mahjong game artificial intelligence (AI) algorithm and its test environment is challenging. Through the analysis of relevant research literature on Mahjong game, this paper identified two types of Mahjong AI construction methods based on knowledge and data. Moreover, the advantages and disadvantages of each typical method are analyzed, emphasizing the construction method of Suphx. The problems and challenges encountered in constructing Mahjong AI are identified, suggesting the need to apply experience replay, hierarchical reinforcement learning, curiosity model, opponent model, metalearning, transfer learning, and curriculum learning to the AI algorithm optimization of Mahjong game and construct diversified Mahjong AI evaluation indicators, general confrontation platforms, and high-quality data sets. These problems are all promising research directions for the future. © 2023, Editorial Department of CAAI Transactions on Intelligent Systems. All rights reserved.     

负增量关联规则更新算法研究

在增量式关联规则更新算法的研究中,关于负增量式更新算法的研究比较少。提出了一种实用的在支持度和置信度不变的情况下数据集规模减小的负增量关联规则更新算法。算法在如何减少数据集的扫描次数,如何充分利用现有的信息减少候选集的规模等方面进行了研究,给出了算法的具体实现。通过分析,算法是可行的。     

负增量关联规则更新算法研究

在增量式关联规则更新算法的研究中，关于负增量式更新算法的研究比较少。提出了一种实用的在支持度和置信度不变的情况下数据集规模减小的负增量关联规则更新算法。算法在如何减少数据集的扫描次数，如何充分利用现有的信息减少候选集的规模等方面进行了研究，给出了算法的具体实现。通过分析，算法是可行的。     

基于机器学习的配电网监控信息智能分析规则库构建方法

为构建一种具有实时性的配电网监控信息智能分析规则库,提出了基于机器学习的配电网监控信息智能分析规则库构建方法。将规则库中全部配电网监控规则头排序并设成主链,将规则导进链表里生成规则集,保证各个监控信息数据包都存在一个分析规则。使用基于机器学习的配电网故障数据分类方法,识别配电网监控信息中的故障数据,并提取故障数据频繁项...     

Preface

Database and Artificial Intelligence (AI) can benefit from each other. On the one hand, AI can make database more intelligent (AI4DB) by exploiting learning-based techniques. On the other hand, database techniques can optimize AI models (DB4AI), such as reducing the complexity of using AI models and accelerating the deployment of AI algorithms. In this special section, we discuss 1) how to exploit AI or machine learning techniques for index design, performance tuning, query processing in database systems, and 2) how to utilize database and data management techniques to make AI models more reusable and more tolerant to dirty data.     

Combining rough decisions for intelligent text mining using Dempster’s rule

An important issue in text mining is how to make use of multiple pieces knowledge discovered to improve future decisions. In this paper, we propose a new approach to combining multiple sets of rules for text categorization using Dempster’s rule of combination. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model classification decisions from multiple sets of rules as pieces of evidence which can be combined by Dempster’s rule of combination. We apply these methods to 10 of the 20-newsgroups—a benchmark data collection (Baker and McCallum 1998), individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data is statistically significant and better than that of the best single set of rules. The comparative analysis between the Dempster–Shafer and the majority voting (MV) methods along with an overfitting study confirm the advantage and the robustness of our approach.     

智能交互的物体识别增量学习技术综述

智能交互系统是研究人与计算机之间进行交流与通信,使计算机能够在最大程度上完成交互者的某个指令的一个领域。其发展的目标是实现人机交互的自主性、安全性和友好性。增量学习是实现这个发展目标的一个途径。本文对智能交互系统的任务、背景和获取信息来源进行简要介绍,主要对增量学习领域的已有工作进行综述。增量学习是指一个学习系统能不断地从新样本中学习新的知识,非常类似于人类自身的学习模式。它使智能交互系统拥有自我学习,提高交互体验的能力。文中对主要的增量学习算法的基本原理和特点进行了阐述,分析各自的优点和不足,并对进一步的研究方向进行展望。     

Study on shop floor control system in semiconductor fabrication by self-organizing map-based intelligent multi-controller

To confirm semiconductor wafer fabrication (FAB) operating characteristics, the scheduling decisions of shop floor control systems (SFCS) must develop a multiple scheduling rules (MSRs) approach in FABs. However, if a classical machine learning approach is used, an SFCS in FABs knowledge base (KB) can be developed by using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. This process becomes an intolerably time-consuming task. In addition, although the best decision rule can be determined for each scheduling decision variable, the combination of all the decision rules may not simultaneously satisfy the global objective function. To address these issues, this study proposes an intelligent multi-controller that incorporates three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSRs selection mechanism. These mechanisms can overcome the long training time problem of the classical machine learning approach in the training examples generation phase. Under various production performance criteria over a long period, the proposed intelligent multi-controller approach yields better system performance than fixed decision scheduling rules for each of the decision variables at the start of each production interval.     

Information fusion as an integrative cross-cutting enabler to achieve robust,explainable, and trustworthy medical artificial intelligence

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.     

一种基于Rough集获取规则知识的增量式学习方法

将Rough集理论应用于规则归纳系统,提出了一种基于粗糙集获取规则知识库的增量式学习方法,能够有效处理决策表中不一致情形,采用启发式算法获取决策表的最简规则,当新对象加入时在原有规则集基础上进行规则知识库的增量式更新,避免了为更新规则而重新运行规获取算法。并用UCI中多个数据集从规则集的规则数目、数据浓缩率、预测能力等指标对该算法进行了测试。实验表明了该算法的有效性。     

云平台下基于粗糙集的并行增量知识更新算法

日益复杂和动态变化的海量数据处理,是当前人们普遍关注的问题,其核心内容之一是研究如何利用已有的信息实现快速的知识更新.粒计算是近年来新兴的一个研究领域,是信息处理的一种新的概念和计算范式,主要用于描述和处理不确定的、模糊的、不完整的和海量的信息,以及提供一种基于粒与粒间关系的问题求解方法.作为粒计算理论中的一个重要组成部分,粗糙集是一种处理不确定性和不精确性问题的有效数学工具.根据云计算中的并行模型MapReduce,给出了并行计算粗糙集中等价类、决策类和两者之间相关性的算法;然后,设计了用于处理大规模数据的并行粗糙近似集求解算法.为应对动态变化的海量数据,结合MapReduce模型和增量更新方法,根据不同的增量策略,设计了两种并行增量更新粗糙近似集的算法.实验结果表明,该算法可以有效地快速更新知识;而且数据量越大,效果越明显.     

智能可视化与可视分析

可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量,捕捉关键信息,并选取最有效的视觉呈现方式,从而使用户更快、更准确、更全面地从可视化中理解数据。利用人工智能方法,交互式可视化系统也能更好地学习用户习惯及用户意图,推荐符合用户需求的可视化形式、交互操作和数据特征,从而降低用户探索的学习及时间成本,提高交互分析的效率。人工智能方法在可视化中的应用受到了极大关注,产生了大量学术成果。本文从最新工作出发,探讨人工智能在可视化流程的关键步骤中的作用。包括如何智能地表示和管理数据、如何辅助用户快速创建和定制可视化、如何通过人工智能扩展交互手段及提高交互效率、如何借助人工智能辅助数据的交互分析等。具体而言,本文详细梳理每个步骤中需要完成的任务及解决思路,介绍相应的人工智能方法（如深度网络结构）,并以图表数据为例介绍智能可视化与可视分析的应用,最后讨论智能可视化方法的发展趋势,展望未来的研究方向及应用场景。     

基于量化规则格的关联规则渐进更新*

提出一种基于量化规则格的规则更新算法,重点讨论了在新增格节点的过程中规则更新的思想,实现在格的渐增生成过程中,相应的关联规则也得到渐进更新.最后给出简单实例说明规则更新算法的正确性.     

基于改进分辨矩阵的增量式数据挖掘模型研究

粗糙集理论是近年来出现的处理模糊和不确定性的数学工具,并已广泛应用于人工智能的许多领域。文章针对在增量式数据环境下挖掘决策规则的特点,在回顾基于分辨矩阵的数据挖掘算法及其不足的基础上,利用决策矩阵的概念有效地处理具有不同决策类的各种决策系统。在此基础上提出相应的对每一个决策类建立决策矩阵的增量式挖掘算法,最后利用算例验证了算法的合理性和有效性。该算法步骤同传统的分辨矩阵算法相比,能在增量式环境下快速而有效地进行确定性规则和可能性规则的学习并对可能性规则建立相应的置信度,使规则的获取更具实用性。     

RMRL: improved regret minimisation techniques using learning automata

Game theory as one of the most progressive areas in AI in last few years originates from the same root as AI. The unawareness of the other players and their decisions in such incomplete-information problems, make it necessary to use some learning techniques to enhance the decision-making process. Reinforcement learning techniques are studied in this research; regret minimisation (RM) and utility maximisation (UM) techniques as reinforcement learning approaches are widely applied to such scenarios to achieve optimum solutions. In spite of UM, RM techniques enable agents to overcome the shortage of information and enhance the performance of their choices based on regrets, instead of utilities. The idea of merging these two techniques are motivated by iteratively applying UM functions to RM techniques. The main contributions are as follows; first, proposing some novel updating methods based on UM of reinforcement learning approaches for RM; the proposed methods refine RM to accelerate the regret reduction, second, devising different procedures, all relying on RM techniques, in a multi-state predator-prey problem. Third, how the approach, called RMRL, enhances different RM techniques in this problem is studied. Estimated results support the validity of RMRL approach comparing with some UM and RM techniques.     

一种高效的关联规则维护算法研究与实现

关联规则挖掘是数据挖掘领域中的重要研究内容之一。由于数据挖掘的过程是动态交互的，因此对已经发现的关联规则进行维护更新显得非常重要。提出了一种实用的在支持度和置信度不变的情况下数据集规模减小的负增量关联规则维护算法。算法在如何减少数据集的扫描次数，如何充分利用现有的信息减少候选集的规模等方面进行了研究，给出了算法的具体实现。理论分析和实验结果表明算法是有效的。     

一种高效的关联规则维护算法研究与实现

关联规则挖掘是数据挖掘领域中的重要研究内容之一。由于数据挖掘的过程是动态交互的,因此对已经发现的关联规则进行维护更新显得非常重要。提出了一种实用的在支持度和置信度不变的情况下数据集规模减小的负增量关联规则维护算法。算法在如何减少数据集的扫描次数,如何充分利用现有的信息减少候选集的规模等方面进行了研究,给出了算法的具体实现。理论分析和实验结果表明算法是有效的。     

Mining fuzzy association rules from low-quality data

Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications have a certain degree of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early childhood were made to verify the performance of the proposed algorithm.