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blogs:pub2016:a_unified_approach_for_measuring_precision_and_generalization_based_on_anti-alignments [2016/10/04 11:03] hverbeek |
blogs:pub2016:a_unified_approach_for_measuring_precision_and_generalization_based_on_anti-alignments [2016/10/04 11:04] (current) hverbeek |
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B.F. van Dongen, J. Carmona, and T. Chatain. [[http:// | B.F. van Dongen, J. Carmona, and T. Chatain. [[http:// | ||
+ | ===== Abstract ===== | ||
+ | |||
+ | The holy grail in process mining is an algorithm that, given an event | ||
+ | log, produces fitting, precise, properly generalizing and simple process models. | ||
+ | While there is consensus on the existence of solid metrics for fitness and simplicity, | ||
+ | current metrics for precision and generalization have important flaws, which | ||
+ | hamper their applicability in a general setting. In this paper, a novel approach to | ||
+ | measure precision and generalization is presented, which relies on the notion of | ||
+ | anti-alignments. An anti-alignment describes highly deviating model traces with | ||
+ | respect to observed behavior. We propose metrics for precision and generalization | ||
+ | that resemble the leave-one-out cross-validation techniques, where individual | ||
+ | traces of the log are removed and the computed anti-alignment assess the | ||
+ | model’s capability to describe precisely or generalize the observed behavior. The | ||
+ | metrics have been implemented in ProM and tested on several examples. | ||
+ | |||
+ | ===== Links ===== | ||
+ | |||
+ | * {{: | ||