Dynamic ensemble selection for data streams and multi-view learning

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，它都优于单个分类器模型。最有前途的MCS方法之一是动态集成选择（DES），其中根据每个待分类的新样本动态选择基本分类器。近年来，由于动态集成系统优于静态集成系统和单片分类器，DES已成为多分类器系统文献中一个活跃的研究课题。尽管最近在DES方法方面取得了进展，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的是一个固定的环境。然而，大多数实际应用程序都具有非静态环境，其中数据通常以数据流的形式出现，并且不断变化。处理非静态环境给DES方法带来了新的挑战，因为它们需要逐步学习并处理数据流中发现的多个问题，例如添加新类、新特性和数据的新视图。其次，目前对DES技术的研究考虑了问题的单一观点（即单一特征空间）。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，如文本分类、生物识别和物联网（IoT），从采用多视图学习方法中受益匪浅。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来实现：(i)发展专门为动态集合选择技术设计的池生成方法；调整动态集合选择以处理大量数据；（三）开发动态多视图集成选择方法。这一研究项目将带来健壮的动态集成模型，这将有利于数据通过流固有的应用，如金融数据分类、假新闻检测和交通控制。此外，我希望DES方法能够显著影响多视图学习领域，因为这种突破性的方法将使我们能够通过选择动态数据中最相关的视图来有效地解决该领域的基本问题。

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.