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A User Driven Data Mining Process Model and Learning System

机译：用户驱动的数据挖掘过程模型和学习系统

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This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application.
机译：本文讨论了在用户无法提供构建预测模型的所有输入的现实情况下使用数据挖掘模型的问题。开发了一种学习系统框架，即基于查询的学习系统（QBLS），以在实践中提高并非所有输入都可用于查询系统的预测模型的性能。开发了一种称为“基于查询的特征选择”（QBFS）的自动特征选择算法，用于选择特征，以获得特征的相对最小子集和相对最大分类精度之间的平衡。 QBLS系统和QBFS算法的性能已在实际应用中成功演示。

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