Meenachi Loganathan, Ramakrishnan Srinivasan
Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India.
Healthc Technol Lett. 2018 Aug 15;5(4):130-135. doi: 10.1049/htl.2018.5041. eCollection 2018 Aug.
Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when compared to other feature selection algorithms.
癌症是人类生命中的致命疾病之一。如果在疾病的早期阶段进行诊断，患者有可能存活。在这篇信函中，作者提出了一种基因搜索模糊粗糙（GSFR）特征选择算法，该算法通过进化顺序基因搜索技术与模糊粗糙集进行混合以选择特征。应用遗传算子的选择、交叉和变异从数据集中生成特征子集。使用正区域和边界区域作为适应度函数，利用模糊粗糙集的修正依赖函数对生成的子集进行评估。特征子集的生成和评估持续进行，直到得到最佳子集以开发分类模型。将所选特征应用于不同的分类器，其中模糊粗糙最近邻（FRNN）分类器在分类准确率和计算时间方面表现最优。因此，将FRNN应用于针对所提出的GSFR特征选择算法对现有特征选择算法的性能分析。与其他特征选择算法相比，所提出的GSFR特征选择算法产生的结果被证明是精确的。
