On active learning of record matching packages
On active learning of record matching packages
复制标题
DOI：
10.1145/1807167.1807252
复制
发表时间：
2010-06
期刊：
影响因子：
--
通讯作者：
A. Arasu;M. Götz;R. Kaushik
中科院分区：
文献类型：
--
作者：
A. Arasu;M. Götz;R. Kaushik
我们考虑在主动学习环境下学习记录匹配包（分类器）的问题。在主动学习中，学习算法选择一组要标记的示例，而不像更传统的被动学习设置，用户选择标记的示例。主动学习对于记录匹配很重要，因为手动识别一组合适的标记示例很困难。以前使用主动学习进行记录匹配的算法有严重的局限性：它们学习的包缺乏质量保证，算法不能扩展到大的输入大小。我们提出了克服这些限制的新算法。我们的算法与传统的主动学习方法有着根本的不同，并且是为了利用特定于记录匹配的问题特征而设计的。我们包括对现实世界数据的详细实验评估，以证明我们的算法的有效性。
We consider the problem of learning a record matching package (classifier) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. Active learning is important for record matching since manually identifying a suitable set of labeled examples is difficult. Previous algorithms that use active learning for record matching have serious limitations: The packages that they learn lack quality guarantees and the algorithms do not scale to large input sizes. We present new algorithms for this problem that overcome these limitations. Our algorithms are fundamentally different from traditional active learning approaches, and are designed ground up to exploit problem characteristics specific to record matching. We include a detailed experimental evaluation on realworld data demonstrating the effectiveness of our algorithms.