论文标题
乳腺癌诊断的神经网络中的双峰分布去除和遗传算法
Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis
论文作者
论文摘要
过去对乳腺癌的诊断进行了很好的研究。已经设计了多个线性编程模型，以近似细胞特征与肿瘤恶性肿瘤之间的关系。但是，这些模型在处理非线性相关性方面的能力较低。神经网络相反，在处理复杂的非线性相关性方面具有强大的功能。因此，通过基于神经网络的模型来解决此癌症诊断问题肯定是有益的。特别是，将偏见引入神经网络训练过程被视为提高训练效率的重要手段。在许多引入人造偏见的流行建议的方法中，双峰分配去除（BDR）提出了理想的效率提高结果，并且实施方面的简单性。但是，本文研究了BDR对目标癌诊断分类问题的有效性，并表明BDR过程实际上会对分类性能产生负面影响。此外，本文还探讨了遗传算法作为特征选择的有效工具，并与基线模型相比，没有任何特征选择，产生了明显的更好的结果
Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing complex non-linear correlations. It is thus certainly beneficial to approach this cancer diagnosis problem with a model based on neural network. Particularly, introducing bias to neural network training process is deemed as an important means to increase training efficiency. Out of a number of popular proposed methods for introducing artificial bias, Bimodal Distribution Removal (BDR) presents ideal efficiency improvement results and fair simplicity in implementation. However, this paper examines the effectiveness of BDR against the target cancer diagnosis classification problem and shows that BDR process in fact negatively impacts classification performance. In addition, this paper also explores genetic algorithm as an efficient tool for feature selection and produced significantly better results comparing to baseline model that without any feature selection in place
