Feature selection and classification of leukocytes using random forest
Feature selection and classification of leukocytes using random forest
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DOI：
10.1007/s11517-014-1200-8
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发表时间：
2014-12-01
影响因子：
3.2
通讯作者：
Arya, K. V.
中科院分区：
文献类型：
--
作者：
Saraswat, Mukesh;Arya, K. V.
在从复杂的组织切片图像的形态背景中自动分割白细胞时，还会提取大量的伪影/噪声，导致产生大量的多变量数据。这种多变量数据降低了分类器区分白细胞和伪影/噪声的性能。然而，与高维特征空间相比，显著特征的选择在降低计算复杂度和提高分类器性能方面起着重要作用。为此，本文提出了一种新的基于基尼重要性的二进制随机森林特征选择方法。此外，使用随机森林分类器将提取的对象分类为伪像、单核细胞和多形核细胞。实验结果表明，与其他特征约简方法相比，该方法有效地剔除了不相关的特征，保持了较高的分类精度。
In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.