Operations Management and Decision Sciences, ESSEC Business School, Paris, France,
Health Care Manag Sci. 2014 Jun;17(2):194-201. doi: 10.1007/s10729-013-9250-2. Epub 2013 Sep 19.
We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive. The heuristic increased predictive accuracy by an average of 0.51 % percent, a small amount. It also increased total agreement between different network learning algorithms (Max Spanning Tree, Taboo, EQ, SopLeq, and Taboo Order) by 25 %. Prior to use of the heuristic, the multiple raters Kappa between the algorithms was 0.60 (95 % confidence interval, CI, from 0.53 to 0.67) indicating moderate agreement among the networks learned through different algorithms. After the use of the heuristic, the multiple raters Kappa was 0.85 (95 % CI from 0.78 to 0.92). There was a statistically significant increase in agreement between the five algorithms (alpha < 0.05). These data suggest that the heuristic increased agreement between networks learned through use of different algorithms, without loss of predictive accuracy. Additional research is needed to see if findings persist in other data sets and to explain why a heuristic used by humans could improve construct validity of mathematical algorithms.
我们研究了一种常见认知启发式在从数据中无监督学习贝叶斯概率网络中的作用。人类感知因果关系之间的关联比诊断关系之间的关联更大。这一心理原则可以用来通过禁止不太具预测性的方向来定向贝叶斯网络中的弧。该启发式平均提高了 0.51%的预测准确性，这是一个很小的提升。它还将不同网络学习算法（最大生成树、禁忌、EQ、SopLeq 和禁忌顺序）之间的总一致性提高了 25%。在使用启发式之前，算法之间的多重评分者 Kappa 值为 0.60（95%置信区间，CI，从 0.53 到 0.67），表明通过不同算法学习的网络之间存在中等一致性。使用启发式后，多重评分者 Kappa 值为 0.85（95%CI 从 0.78 到 0.92）。五个算法之间的一致性有统计学显著增加（alpha<0.05）。这些数据表明，该启发式提高了通过使用不同算法学习的网络之间的一致性，而不会损失预测准确性。需要进一步研究以确定在其他数据集是否存在类似结果，并解释为什么人类使用的启发式可以提高数学算法的构建有效性。
