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首页> 外文期刊>Knowledge-Based Systems >MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology
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MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology

机译：MEFES：一种用于在子组发现中检测异常的进化建议。在聚光光伏技术中的应用

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摘要

Subgroup discovery is a broadly applicable data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. The obtaining of general rules describing as many instances as possible is preferred in subgroup discovery, but this can lead to less accurate descriptions that incorrectly describe some instances. Under certain conditions, these incorrectly-described instances can be grouped into exceptions. A new post-processing methodology for the detection of exceptions associated to previously discovered subgroups is presented in this paper. The purpose is to obtain a new description to improve the accuracy of the initial subgroup and to describe new small spaces in data with unusual behaviour within the subgroup. This post-processing methodology can be applied to the results of any subgroup discovery algorithm. A post-processing multiobjective evolutionary fuzzy system is developed following this methodology, the Multiobjective Evolutionary Fuzzy system for the detection of Exceptions in Subgroups (MEFES). A wide experimental study has been performed, supported by statistical tests, comparing the results obtained by representative subgroup discovery algorithms with those obtained after applying the post-processing algorithm. Finally, MEFES is applied in a real problem related to the description of the behaviour of a type of solar cell in the Concentrating Photovoltaic area providing useful information to the experts.
机译：子组发现是一种广泛适用的数据挖掘技术，其主要目的是搜索在统计上就感兴趣的属性而言异常的数据子组的描述。在子组发现中，首选获得描述尽可能多实例的通用规则，但这会导致描述不正确的描述，从而错误地描述了某些实例。在某些情况下，这些描述不正确的实例可以分组为例外。本文提出了一种新的后处理方法，用于检测与先前发现的子组相关的异常。目的是获得一个新的描述，以提高初始子组的准确性，并描述子组内具有异常行为的数据中的新小空间。这种后处理方法可以应用于任何子组发现算法的结果。遵循这种方法开发了一种后处理多目标进化模糊系统，即用于检测子组中的异常的多目标进化模糊系统（MEFES）。在统计测试的支持下，已经进行了广泛的实验研究，将代表性的子组发现算法获得的结果与应用后处理算法后获得的结果进行了比较。最后，MEFES应用于与聚光光伏区域中某类型太阳能电池的行为描述有关的实际问题，为专家提供了有用的信息。

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