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On the consistency of information filters for lazy learning algorithms

机译:惰性学习算法信息过滤的一致性

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A common practice when filtering a case-base is to employ a filtering scheme that decides which cases to delete,as well as how many cases to delete,such that the storage requirements are minimized and the classification competence is preserved or improved.We introduce an algorithm that rivals the most successful existiang algorithm in the average case when filtering 30 classification problems.neither algorithm consistently outperforms the other,with each performing well on different problems.Consistency over many domains,we argue,is very hard to achieve when deploying a filtering algorithm.
机译:过滤案例库时的一种常见做法是采用一种过滤方案,该方案确定要删除的案例以及要删除的案例数量,以使存储需求最小化,并保留或提高分类能力。在过滤30个分类问题时,该算法在一般情况下可与最成功的存在算法相抗衡。这两种算法都无法始终胜过另一种算法,并且在不同问题上的表现都很好。我们认为,在许多领域的一致性在部署过滤时很难实现算法。

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