Since recommended treatment for Non-small cell lung cancer (NSCLC) after surgery is chemotherapy, the prediction of effectiveness or futileness of adjuvant chemotherapy (ACT) in early stage is important for future decision. Classification of NSCLC in gene expression data is performed to predict effectiveness or futileness of ACT. Selection of genes highly correlated with the class attribute, affects the classification accuracy. In this paper, a new cell separation algorithm is proposed which it imitates the action of cell separation using differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA uses the application of centrifugal force to separate the solutions based on their objective function in different steps while the velocity is increased in each step. The CSA contributes to automatic trade-off between exploration and exploitation by control of selection rate during the search process. To examine the CSA, 25 test functions were used first and then the CSA was applied to predict effectiveness or futileness of ACT. The number of genes in candidate subsets is handled by increasing the subset size if after a certain number of iterations there is no improvement in fitness of the subset. This contributes to less time consideration and memory usage. In this experiment, the NSCLC data contain 280 samples collected from four institutes are used. As results, the minimum number of five genes with dependency degree equal to one and classification accuracy of higher than 94% for SVM, KNN and MLP classifiers is obtained.

由于手术后非小细胞肺癌（NSCLC）的推荐治疗方法是化疗，因此早期预测辅助化疗（ACT）的有效性或无效性对于将来的决策至关重要。进行基因表达数据中NSCLC的分类以预测ACT的有效性或无效性。与分类属性高度相关的基因的选择会影响分类的准确性。在本文中，提出了一种新的细胞分离算法，该算法模拟了使用涉及多个离心步骤并在每个步骤中提高转子速度的差分离心过程进行细胞分离的作用。CSA使用离心力根据目标函数在不同步骤中分离解决方案，同时在每个步骤中提高速度。通过在搜索过程中控制选择率，CSA有助于自动在勘探与开发之间进行权衡。为了检查CSA，首先使用25个测试功能，然后将CSA应用于预测ACT的有效性或无效性。如果经过一定数量的迭代后，子集的适合度没有改善，则通过增加子集大小来处理候选子集中的基因数量。这有助于减少时间和内存使用量。在此实验中，使用了从四个机构收集的280个样本的NSCLC数据。结果，SVM的五个最小依赖关系度等于1且分类精度高于94％的基因，