In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length.

中文翻译：

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一种计算最小长度不可约测试器的算法

在模式识别中，消除不必要和/或冗余的属性被称为特征选择。不可约测试器已被用于执行此任务。应用于模式识别和数据挖掘中的特征选择的最小描述长度原则 (MDL) 的一个目标是在数据集中选择最少数量的属性。因此，MDL 原则引导我们考虑最小长度的不可约测试者的子集。文献中已经报道了一些找到整个不可约测试集的算法。然而，这些算法都没有设计为仅生成最小长度的不可约测试器。在本文中，我们提出了第一个专门设计用于从训练样本计算所有最小长度不可约测试者的算法。