Dynamic ensemble selection for data streams and multi-view learning

数据流和多视图学习的动态集成选择

• 批准号: RGPIN-2021-04130
• 负责人: MenelauOliveiraeCruz, Rafael
• 金额: $ 2.11万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742851
• 关键词: Dynamic; ensemble; selection; data; streams

Multiple Classifier System (MCS) is an active area of research in machine learning and pattern recognition. Several studies have been published demonstrating its advantages over individual classifier models either from the theoretical or empirical points of view. One of the most promising MCS approaches is Dynamic Ensemble Selection (DES), in which the base classifiers are selected on the fly, according to each new sample to be classified. DES has become an active research topic in the multiple classifier systems literature in past years due to recent works reporting dynamic ensembles' superior performance over static ones and monolithic classifiers. Despite the recent advancement in DES methods, there are still vastly unexplored areas by the DES community. Firstly, most of the conducted research considers a stationary environment. However, the majority of real-world applications have a non-static environment where data usually comes in the form of data streams and is continuously changing. Handling non-static environments pose new challenges for DES methods since they need to learn incrementally and cope with multiple problems found in data streams such as the addition of new classes, new features, and new views of the data. Secondly, current research on DES techniques considers a single view of the problem (i.e., a single feature space). However, often a single-view data cannot properly describe all examples in the data, and a multi-view approach is therefore required to improve generalization performance. Several real-world applications, such as text classification, biometrics, and the Internet of things (IoT), greatly benefit from adopting a multi-view learning approach. Hence, this research program's main objective is to propose DES methodologies to deal with large data streams and multi-view learning. This objective will be handled through three steps: (i) Development of a pool generation approach specially crafted for dynamic ensemble selection techniques; (ii) Adaptation of dynamic ensemble selection for dealing with large volumes of data; and (iii) Development of a dynamic multi-view ensemble selection methodology. This research program will lead to robust dynamic ensemble models that will benefit applications where data comes inherently through streams such as financial data classification, fake news detection, and traffic control. Moreover, I expect DES methods to significantly impact the field of multi-view learning as this breakthrough methodology will allow us to efficiently solve fundamental problems in this field by selecting the most relevant views of the data on-the-fly.

多分类器系统（MCS）是机器学习和模式识别领域的一个活跃研究领域。已经发表的几项研究表明，无论是从理论还是经验的角度来看，它都比单个分类器模型具有优势。最有前途的MCS方法之一是动态包围选择（DES），其中根据每个待分类的新样本动态地选择基本分类器。DES已经成为一个活跃的研究课题，在多分类器系统的文献中，在过去的几年中，由于最近的工作报告动态集成的上级性能优于静态的和单片分类器。尽管DES方法最近取得了进展，但DES社区仍有大量未开发的领域。首先，大多数进行的研究考虑静止环境。然而，大多数现实世界的应用程序都有一个非静态环境，其中数据通常以数据流的形式出现，并且不断变化。处理非静态环境对DES方法提出了新的挑战，因为它们需要增量学习并科普数据流中发现的多个问题，例如添加新类，新功能和新的数据视图。其次，目前对DES技术的研究考虑了问题的单一观点（即，单个特征空间）。然而，通常单视图数据不能正确地描述数据中的所有示例，因此需要多视图方法来提高泛化性能。一些现实世界的应用程序，如文本分类，生物识别和物联网（IoT），极大地受益于采用多视图学习方法。因此，本研究计划的主要目标是提出DES方法来处理大数据流和多视图学习。这一目标将通过三个步骤实现：㈠开发专门为动态集合选择技术设计的数据库生成方法; ㈡调整动态集合选择以处理大量数据; ㈢开发动态多视图集合选择方法。该研究计划将产生强大的动态集成模型，这些模型将有利于数据本身通过金融数据分类，假新闻检测和交通控制等流来的应用。此外，我预计DES方法将对多视图学习领域产生重大影响，因为这种突破性的方法将使我们能够通过选择最相关的数据视图来有效地解决该领域的基本问题。