用多模型学习构造相关特征集
Structuring Relevant Feature Sets with Multiple Model Learning
摘要：
特征选择是最重要的学习任务之一，特别是在高维数据集中，其目标是了解学习数据集的机制。然而，它们中的大多数通常只提供一组平坦的相关特征，并且不提供关于什么样的结构（例如，特征分组）可能是相关特征集的基础的进一步信息。在本文中，我们提出了一个新的学习范式，我们的目标是揭示的结构，为一个给定的学习问题的相关功能集。我们发现了两种类型的特征集，不可替换的特征，包含有关目标变量的重要信息，不能被其他特征取代，功能相似的特征集，可以在学习模型中互换使用，考虑到不可替换的特征的存在，预测性能没有变化。为此，我们提出了一种新的学习算法，学习了一些不相交的模型，使用模型不相交正则化约束以及对不相交模型的预测协议的约束。我们探讨了我们的方法在一些高维数据集上的行为，并表明，正如其建设所预期的那样，这些满足一些属性。也就是说，模型不相交性，高预测一致性，以及与在全套相关特征上学习的模型相似的预测性能。以这种方式构造相关特征集的能力可以成为科学知识发现的不同应用中的有价值的工具。
英文摘要：
Feature selection is one of the most prominent learning tasks, especially in high-dimensional datasets in which the goal is to understand the mechanisms that underly the learning dataset. However most of them typically deliver just a flat set of relevant features and provide no further information on what kind of structures, e.g. feature groupings, might underly the set of relevant features. In this paper we propose a new learning paradigm in which our goal is to uncover the structures that underly the set of relevant features for a given learning problem. We uncover two types of features sets, non-replaceable features that contain important information about the target variable and cannot be replaced by other features, and functionally similar features sets that can be used interchangeably in learned models, given the presence of the non-replaceable features, with no change in the predictive performance. To do so we propose a new learning algorithm that learns a number of disjoint models using a model disjointness regularization constraint together with a constraint on the predictive agreement of the disjoint models. We explore the behavior of our approach on a number of high-dimensional datasets, and show that, as expected by their construction, these satisfy a number of properties. Namely, model disjointness, a high predictive agreement, and a similar predictive performance to models learned on the full set of relevant features. The ability to structure the set of relevant features in such a manner can become a valuable tool in different applications of scientific knowledge discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
