Customized classification learning based on query projections
Customized classification learning based on query projections
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DOI：
10.1016/j.ins.2007.02.030
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Yiqiu Han;Wai Lam;C. Ling
中科院分区：
文献类型：
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作者：
Yiqiu Han;Wai Lam;C. Ling
我们开发了一个定制的分类学习方法QPL，这是基于查询投影。给定一个要分类的实例（查询实例），QPL探索查询实例（QP）的投影，这些投影本质上是查询和训练实例共享的属性值的子集。QPL调查QP的相关训练数据分布，以决定它是否有用。通过组合所选择的有用QP的一些统计数据来进行查询的最终预测。与现有的基于实例的学习不同，QPL不需要计算实例之间的距离度量。利用QP进行学习可以探索更丰富的假设空间，并在精度和鲁棒性之间实现平衡。QPL的另一个特点是，目标类可能会因给定数据集中的不同查询实例而异。我们已经评估了我们的方法与合成和基准数据集。结果表明，QPL可以实现良好的性能和高可靠性。
We develop a customized classification learning method QPL, which is based on query projections. Given an instance to be classified (query instance), QPL explores the projections of the query instance (QPs), which are essentially subsets of attribute values shared by the query and training instances. QPL investigates the associated training data distribution of a QP to decide whether it is useful. The final prediction for the query is made by combining some statistics of the selected useful QPs. Unlike existing instance-based learning, QPL does not need to compute a distance measure between instances. The utilization of QPs for learning can explore a richer hypothesis space and achieve a balance between precision and robustness. Another characteristic of QPL is that the target class may vary for different query instances in a given data set. We have evaluated our method with synthetic and benchmark data sets. The results demonstrate that QPL can achieve good performance and high reliability.