Instance and variable selection involve identifying a subset of instances and variables such that the learning process will use only this subset with better performances and lower cost. Due to the huge amount of data available in many fields, data reduction is considered as an NP-hard problem. In this paper, we present a simultaneous instance and variable selection approach based on the Random Forest-RI ensemble methods in the aim to discard noisy and useless information from the original data set. We proposed a selection principle based on two concepts: the ensemble margin and the importance variable measure of Random Forest-RI. Experiments were conducted on cytological images for the automatic segmentation and recognition of white blood cells WBC (nucleus and cytoplasm). Moreover, in order to explore the performance of our proposed approach, experiments were carried out on standardized datasets from UCI and ASU repository, and the obtained results of the instances and variable selection by the Random Forest classifier are very encouraging.

中文翻译：

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基于像素的分类中的实例和变量选择方法，用于白细胞自动分割

实例和变量选择涉及识别实例和变量的子集，以便学习过程将仅使用具有更好性能和更低成本的该子集。由于在许多领域中都有大量可用数据，因此数据缩减被认为是NP难题。在本文中，我们提出了一种基于随机森林-RI集成方法的同时实例和变量选择方法，目的是丢弃原始数据集中的嘈杂和无用信息。我们基于两个概念提出了一种选择原则：整体边缘和Random Forest-RI的重要性变量度量。在细胞学图像上进行了实验，以自动分割和识别白细胞WBC（细胞核和细胞质）。此外，为了探索我们提出的方法的效果，