Classical machine learning models typically try to optimize the model based on the most discriminatory features of the data; however, they do not usually account for end user preferences. In certain applications, this can be a serious issue as models not aware of user preferences could become costly, untrustworthy, or privacy-intrusive to use, thus becoming irrelevant and/or uninterpretable. Ideally, end users with domain knowledge could propose preferable features that the predictive model could then take into account. In this paper, we propose a generic modeling method that respects end user preferences via a relative ranking system to express multi-criteria preferences and a regularization term in the model's objective function to incorporate the ranked preferences. In a more generic perspective, this method is able to plug user preferences into existing predictive models without creating completely new ones. We implement this method in the context of decision trees and are able to achieve a comparable classification accuracy while reducing the use of undesirable features.

中文翻译：

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具有最终用户偏好的预测模型

经典机器学习模型通常会尝试根据数据中最具辨别力的特征来优化模型；然而，它们通常不考虑最终用户的偏好。在某些应用程序中，这可能是一个严重的问题，因为不知道用户偏好的模型使用起来可能会变得昂贵、不可信或侵犯隐私，从而变得无关紧要和/或无法解释。理想情况下，具有领域知识的最终用户可以提出更可取的特征，然后预测模型可以考虑这些特征。在本文中，我们提出了一种通用建模方法，该方法通过相对排名系统来表达多标准偏好和模型目标函数中的正则化项以纳入排名偏好。从更一般的角度来看，这种方法能够将用户偏好插入现有的预测模型中，而无需创建全新的模型。我们在决策树的上下文中实现了这种方法，并且能够在减少不良特征的使用的同时实现可比较的分类精度。