多任务间构件迁移的相关性度量
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
摘要：
多任务学习是一种并行处理多个不同任务并在其中传递知识的学习范式。XOF是一种学习分类器系统，使用基于树的程序来编码构建块（元特征），构建并收集具有丰富判别信息的特征，用于观察列表中的分类任务。本文旨在促进自动化的功能转移之间的任务，利用观察列表。我们假设分类任务的最佳区分特征具有其特征。因此，任何两个任务之间的相关性可以通过比较它们最合适的模式来估计。我们提出了一个多XOF系统，称为mXOF，可以动态地适应XOF之间的功能转移。该系统利用观察列表来估计任务相关性。该方法实现了特征传递的自动化。在知识发现方面，相似性估计提供了多个数据之间的深刻关系。我们在各种场景上进行了mXOF实验，例如代表性的分层布尔问题，UCI Zoo数据集中不同类别的分类以及不相关的任务，以验证其自动知识转移和估计任务相关性的能力。实验结果表明，mXOF算法能够合理地估计多个任务之间的相关性，并通过动态特征迁移来提高学习性能。
英文摘要：
Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an observed list. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the observed list to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledge-transfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.
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。
