CRISP: Consensus Regularized Selection based Prediction
CRISP: Consensus Regularized Selection based Prediction
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DOI：
10.1145/2983323.2983779
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发表时间：
2016-10
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影响因子：
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通讯作者：
Ping Wang;Karthik K. Padthe;B. Vinzamuri;Chandan K. Reddy
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文献类型：
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作者：
Ping Wang;Karthik K. Padthe;B. Vinzamuri;Chandan K. Reddy
将正则化方法与标准损失函数（如最小二乘、铰链损失等）相结合，在回归框架内学习具有较低方差和较好泛化能力的预测模型已成为研究人员的热门选择。正则化器还有助于用高维数据构建可解释的模型，这使得它们非常有吸引力。据观察，每个正则化唯一制定，以捕捉数据的特定属性，如相关性，结构稀疏性和时间平滑。在学习预测模型的同时，在这些不同的正则化器之间获得共识的问题对于确定问题的最佳正则化器是非常重要的。这种方法的优点是，它保留了通过选择单个候选模型学习的最终模型的简单性，这与集成方法的情况不同，因为它们使用多个候选模型进行预测。这被称为共识正则化问题，由于从集成正则化框架中学习和选择模型的固有困难，该问题在文献中并未受到太多关注。为了解决这个问题，在本文中，我们提出了一种方法来产生一个委员会的非凸正则化线性回归模型，并使用一致性准则来确定最佳的预测模型。每个相应的非凸优化问题的委员会有效地解决使用循环坐标下降算法与广义阈值算子。我们的共识正则化选择为基础的预测（CRISP）模型进行评估的电子健康记录（EHR）从一家大型医院的充血性心力衰竭再入院预测问题。我们还在高维合成数据集上评估了我们的模型，以评估其性能。结果表明，CRISP优于几个国家的最先进的方法，如添加剂，基于相互作用和其他竞争的非凸正则化线性回归方法。
Integrating regularization methods with standard loss functions such as the least squares, hinge loss, etc., within a regression framework has become a popular choice for researchers to learn predictive models with lower variance and better generalization ability. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. It is observed that each regularizer is uniquely formulated in order to capture data-specific properties such as correlation, structured sparsity and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers while learning a predictive model is extremely important in order to determine the optimal regularizer for the problem. The advantage of such an approach is that it preserves the simplicity of the final model learned by selecting a single candidate model which is not the case with ensemble methods as they use multiple candidate models for prediction. This is called the consensus regularization problem which has not received much attention in the literature due to the inherent difficulty associated with learning and selecting a model from an integrated regularization framework. To solve this problem, in this paper, we propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator. Our Consensus RegularIzation Selection based Prediction (CRISP) model is evaluated on electronic health records (EHRs) obtained from a large hospital for the congestive heart failure readmission prediction problem. We also evaluate our model on high-dimensional synthetic datasets to assess its performance. The results indicate that CRISP outperforms several state-of-the-art methods such as additive, interactions-based and other competing non-convex regularized linear regression methods.