Extracting optimal actionable plans from additive tree models

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• 作者：Qiang Lu ; Zhicheng Cui ; Yixin Chen ; Xiaoping Chen
• 关键词：actionable knowledge extraction ; machine learning ; additive tree models ; state space search
• 刊名：Frontiers of Computer Science
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：11
• 期：1
• 页码：160-173
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general;
• 出版者：Higher Education Press
• ISSN：2095-2236
• 卷排序：11

文摘

Although amazing progress has been made in machine learning to achieve high generalization accuracy and efficiency, there is still very limited work on deriving meaningful decision-making actions from the resulting models. However, in many applications such as advertisement, recommendation systems, social networks, customer relationship management, and clinical prediction, the users need not only accurate prediction, but also suggestions on actions to achieve a desirable goal (e.g., high ads hit rates) or avert an undesirable predicted result (e.g., clinical deterioration). Existing works for extracting such actionability are few and limited to simple models such as a decision tree. The dilemma is that those models with high accuracy are often more complex and harder to extract actionability from.