反对奥卡姆剃刀实用性的进一步实验证据
Further Experimental Evidence against the Utility of Occam's Razor
摘要：
本文提出了新的实验证据反对奥卡姆剃刀的效用。针对C4.5生成的决策树提出了一个较系统的后处理过程。这个过程是通过拒绝奥卡姆剃刀而得出的，而是考虑到类似物体可能属于同一类的假设。它增加了决策树的复杂性，而不会改变该树在从中推断出它的训练数据上的性能。所得到的更复杂的决策树被证明，平均而言，对于各种常见的学习任务，比不太复杂的原始决策树具有更高的预测精度。这一结果引起了对奥卡姆剃刀效用的相当大的怀疑，因为它通常应用于现代机器学习。
英文摘要：
This paper presents new experimental evidence against the utility of Occam's razor. A~systematic procedure is presented for post-processing decision trees produced by C4.5. This procedure was derived by rejecting Occam's razor and instead attending to the assumption that similar objects are likely to belong to the same class. It increases a decision tree's complexity without altering the performance of that tree on the training data from which it is inferred. The resulting more complex decision trees are demonstrated to have, on average, for a variety of common learning tasks, higher predictive accuracy than the less complex original decision trees. This result raises considerable doubt about the utility of Occam's razor as it is commonly applied in modern machine learning.
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