论文标题
从微阵列表达数据中选择基因：一种具有自适应k-nearest邻里的多目标PSO
Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood
论文作者
论文摘要
癌症检测是医学领域的关键研究主题之一。准确检测不同的癌症类型对于提供更好的治疗设施和对患者的风险最小化非常有价值。本文通过使用基因表达数据来处理人类癌症疾病的分类问题。它提出了一种新的方法来分析微阵列数据集并有效地对癌症疾病进行分类。新方法首先采用信号与噪声比（SNR）来找到一小部分非冗余基因的列表。然后，在归一化之后，将其用于特征选择和使用自适应K-Nearest（KNN）进行癌症疾病分类的多物体颗粒群优化（MOPSO）。该方法通过减少特征数量来提高癌症分类的分类准确性。通过在五个癌症数据集中对癌症疾病进行分类来评估所提出的方法。将结果与最新方法进行比较，这提高了每个数据集的分类精度。
Cancer detection is one of the key research topics in the medical field. Accurate detection of different cancer types is valuable in providing better treatment facilities and risk minimization for patients. This paper deals with the classification problem of human cancer diseases by using gene expression data. It is presented a new methodology to analyze microarray datasets and efficiently classify cancer diseases. The new method first employs Signal to Noise Ratio (SNR) to find a list of a small subset of non-redundant genes. Then, after normalization, it is used Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest Neighborhood (KNN) for cancer disease classification. This method improves the classification accuracy of cancer classification by reducing the number of features. The proposed methodology is evaluated by classifying cancer diseases in five cancer datasets. The results are compared with the most recent approaches, which increases the classification accuracy in each dataset.
