基于特征上下文变化的模型重构
Model Reframing by Feature Context Change
摘要：
特征空间（包括输入和输出变量）表征数据采掘问题。在预测（监督）问题中，特征的质量和可用性决定了因变量的可预测性，以及数据采掘模型在误分类或回归错误方面的性能。然而，好的特征通常很难获得。通常情况下，许多实例都带有缺失值，要么是因为给定属性的实际值不可用，要么是因为它太昂贵。这通常被解释为效用或成本敏感的学习困境，在这种情况下，在错误分类（或回归错误）成本和属性测试成本之间。错误分类成本（MC）和测试成本（TC）可以集成到一个单一的措施，称为联合成本（JC）。我们引入了可以与任何现成的预测技术（包括集成）一起工作的方法和图（例如所谓的JROC图），以便我们在部署时重新构建模型以使用适当的属性子集（功能配置）。换句话说，模型使用可用属性进行训练（一劳永逸），然后通过在被认为对降低联合成本无效的属性上设置缺失值来部署。由于特征配置组合的数量随特征数量呈指数级增长，我们引入了二次方法，该方法能够近似最优配置和模型选择，如实验结果所示。
英文摘要：
The feature space (including both input and output variables) characterises a data mining problem. In predictive (supervised) problems, the quality and availability of features determines the predictability of the dependent variable, and the performance of data mining models in terms of misclassification or regression error. Good features, however, are usually difficult to obtain. It is usual that many instances come with missing values, either because the actual value for a given attribute was not available or because it was too expensive. This is usually interpreted as a utility or cost-sensitive learning dilemma, in this case between misclassification (or regression error) costs and attribute tests costs. Both misclassification cost (MC) and test cost (TC) can be integrated into a single measure, known as joint cost (JC). We introduce methods and plots (such as the so-called JROC plots) that can work with any of-the-shelf predictive technique, including ensembles, such that we re-frame the model to use the appropriate subset of attributes (the feature configuration) during deployment time. In other words, models are trained with the available attributes (once and for all) and then deployed by setting missing values on the attributes that are deemed ineffective for reducing the joint cost. As the number of feature configuration combinations grows exponentially with the number of features we introduce quadratic methods that are able to approximate the optimal configuration and model choices, as shown by the experimental results.
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。
