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摘要翻译：
元挖掘的概念是近年来出现的，它从两个方面扩展了传统的元学习。首先，它不学习只支持学习算法选择任务的元模型，而是支持整个数据挖掘过程的元模型。另外，它摒弃了元学习中的所谓黑箱算法描述方法。现在除了数据集，算法还有描述符和工作流。对于后两者，这些描述是语义的，描述算法的性质。随着数据集和数据挖掘工作流描述符的可用性，元学习中遵循的传统建模技术（通常基于分类和回归算法）不再适用。相反，我们面临着一个问题，其性质更类似于推荐系统中出现的问题。最重要的元挖掘需求是建议应该只使用数据集和工作流描述符以及冷启动问题，例如为新的数据集提供工作流建议。在本文中，我们对元挖掘建模问题采取了不同的观点，将其视为一个推荐问题。为了解释元挖掘的特殊性，我们提出了一种新的基于度量的学习推荐方法。我们的方法学习两个同构度量，一个在数据集和工作流空间，一个在数据集-工作流空间。所有学习到的度量都反映了从数据集-工作流偏好矩阵中建立的相似性。我们展示了我们在生物（微阵列数据集）问题上的元挖掘方法。该方法的应用不仅限于元挖掘问题，其公式具有足够的通用性，可以应用于具有类似要求的问题。
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英文标题：
《Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in
Meta-Mining》
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作者：
Phong Nguyen, Jun Wang, Melanie Hilario and Alexandros Kalousis
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining process. In addition it abandons the so called black-box approach to algorithm description followed in meta-learning. Now in addition to the datasets, algorithms also have descriptors, workflows as well. For the latter two these descriptions are semantic, describing properties of the algorithms. With the availability of descriptors both for datasets and data mining workflows the traditional modelling techniques followed in meta-learning, typically based on classification and regression algorithms, are no longer appropriate. Instead we are faced with a problem the nature of which is much more similar to the problems that appear in recommendation systems. The most important meta-mining requirements are that suggestions should use only datasets and workflows descriptors and the cold-start problem, e.g. providing workflow suggestions for new datasets. In this paper we take a different view on the meta-mining modelling problem and treat it as a recommender problem. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulations is general enough so that it can be applied on problems with similar requirements.
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PDF链接：
https://arxiv.org/pdf/1210.1317
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