用效应量筛选特征，提升乳腺癌检测模型精度
Effect sizes as a statistical feature-selector-based learning to detect breast cancer
- 基于效应量挑选细胞核图像特征，降低数据维度
- 线性核SVM分类器准确率超90%
- 适合医学图像分析与特征选择研究者
尽管在乳腺癌检测领域已投入大量研究，该问题仍具挑战性。效应量是衡量两个变量间关系强度的统计指标。特征选择通过选取部分预测变量来降低数据维度，提升学习模型性能。本文提出一种基于参数化效应量度量的统计特征选择算法，利用从细胞核图像中提取的特征进行降维。实验表明，采用线性核支持向量机（SVM）作为学习工具时，分类准确率超过90%。优异结果表明，效应量可作为有效的特征选择方法，符合当前特征选择标准。
原文摘要 · Abstract (English)
Breast cancer detection is still an open research field, despite a tremendous effort devoted to work in this area. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. Feature selection is widely used to reduce the dimensionality of data by selecting only a subset of predictor variables to improve a learning model. In this work, an algorithm and experimental results demonstrate the feasibility of developing a statistical feature-selector-based learning tool capable of reducing the data dimensionality using parametric effect size measures from features extracted from cell nuclei images. The SVM classifier with a linear kernel as a learning tool achieved an accuracy of over 90%. These excellent results suggest that the effect size is within the standards of the feature-selector methods
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