Crowdsourcing Predictors of Behavioral Outcomes
Crowdsourcing Predictors of Behavioral Outcomes
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DOI：
10.1109/tsmca.2012.2195168
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发表时间：
2013-01-01
影响因子：
8.7
通讯作者：
Lu, Zhenyu
中科院分区：
文献类型：
--
作者：
Bongard, Josh C.;Hines, Paul D. H.;Lu, Zhenyu
从大型数据集生成模型，以及确定要挖掘哪些数据子集，正变得越来越自动化。然而，选择收集什么数据首先需要人类的直觉或经验，通常由领域专家提供。本文描述了一种机器科学的新方法，它第一次证明了非领域专家可以集体制定特征并为这些特征提供值，以便他们能够预测一些感兴趣的行为结果。这是通过建立一个网络平台来实现的，在这个平台上，人类群体可以互动，回答可能有助于预测行为结果的问题，并向同龄人提出新的问题。这导致了动态增长的在线调查，但这种合作行为的结果也导致了可以根据用户对用户生成的调查问题的响应来预测用户结果的模型。在这里，我们描述了两个基于网络的实验，它们实例化了这种方法：第一个网站导致了可以预测用户每月电能消耗的模型，另一个网站导致了可以预测用户身体质量指数的模型。由于在成功的在线协作社区中经常观察到内容的指数增长，所提出的方法在未来可能会导致对行为结果的因果因素的发现和洞察的类似的指数增长。
Generating models from large data sets-and determining which subsets of data to mine-is becoming increasingly automated. However, choosing what data to collect in the first place requires human intuition or experience, usually supplied by a domain expert. This paper describes a new approach to machine science which demonstrates for the first time that nondomain experts can collectively formulate features and provide values for those features such that they are predictive of some behavioral outcome of interest. This was accomplished by building a Web platform in which human groups interact to both respond to questions likely to help predict a behavioral outcome and pose new questions to their peers. This results in a dynamically growing online survey, but the result of this cooperative behavior also leads to models that can predict the user's outcomes based on their responses to the user-generated survey questions. Here, we describe two Web-based experiments that instantiate this approach: The first site led to models that can predict users' monthly electric energy consumption, and the other led to models that can predict users' body mass index. As exponential increases in content are often observed in successful online collaborative communities, the proposed methodology may, in the future, lead to similar exponential rises in discovery and insight into the causal factors of behavioral outcomes.