Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient methods become unsuitable to solve some problems. In this work, a new algorithm was introduced in order to reduce the long runtimes involved in the search of typical testors. The performance of the proposed algorithm was evaluated by means of several tests, which use both real-world and simulation data. MATLAB and Java language on Eclipse SDK platform were used to build the simulation dataset and to perform the tests, respectively. The runtimes achieved by the proposed algorithm were significantly shorter than those obtained by fast-BR and GCreduct (the two fastest algorithms) mainly when the latter ones exhibited excessively long runtimes.

中文翻译：

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RoPM：一种基于基本矩阵递归约简的典型测试器计算算法


特征选择在模式识别和智能计算中发挥着重要作用。全套典型测试器构成了解决特征选择问题的有用工具，特别是那些通过定量和定性特征描述对象的问题。然而，寻找典型的测试者需要很高的计算成本。这就是为什么即使是最有效的方法也变得不适合解决某些问题。在这项工作中，引入了一种新算法，以减少搜索典型测试者所需的长时间运行时间。通过使用真实数据和模拟数据的多次测试来评估所提出算法的性能。 Eclipse SDK平台上的MATLAB和Java语言分别用于构建仿真数据集和进行测试。该算法实现的运行时间明显短于 fast-BR 和 GCreduct（两种最快的算法），主要是因为后者的运行时间过长。