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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >A novel filter approach for efficient selection and small round blue-cell tumor cancer detection using microarray gene expression data
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A novel filter approach for efficient selection and small round blue-cell tumor cancer detection using microarray gene expression data

机译：利用微阵列基因表达数据的新型筛选方法可有效选择和检测小圆形蓝细胞肿瘤癌

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摘要

Feature selection is a vital task in machine learning and data mining to reduce the dimensionality of the data and also improves the classification performance of an algorithm in terms of high precision, low computational cost, and low vulnerability. Various many technologies have been successfully applied in the previous experimental studies for tumor detection. The foremost challenging task of gene selection method is extracting informative genes contribution in the classification from the DNA microarray datasets with lesser computational load. In this paper, we propose the conglomeration of the Kendall Correlation (KC) and Filter based Feature Selection (FS) method for better classification and prediction. We demonstrate the extensive comparison of the effect of Kendall Correlation with FS methods, using Relief-F, Joint Mutual Information (JMI), and Mutual Information based feature selection (MIFS), Conditional mutual information maximization (CMIM), and Max-relevance & Min-Redundancy (mRMR). To measure the classification performance of four diverse supervised classifiers K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Naïve Bayes (NB), and Decision Tree (DT) have been used on small round blue cell tumors (SRBCT) dataset. The result demonstrates that Kendall Correlation in accumulation with mRMR performances better than other combinations.
机译：特征选择是机器学习和数据挖掘中至关重要的任务，它在降低数据的维数方面也具有很高的精度，较低的计算成本和较低的脆弱性，从而提高了算法的分类性能。在先前的肿瘤检测实验研究中已经成功应用了多种技术。基因选择方法的首要任务是从具有较少计算量的DNA微阵列数据集中提取信息丰富的基因在分类中的贡献。在本文中，我们提出了Kendall相关（KC）和基于过滤器的特征选择（FS）方法的结合，以实现更好的分类和预测。我们使用救济-F，联合互信息（JMI）和基于互信息的特征选择（MIFS），条件互信息最大化（CMIM）以及最大相关性和最低冗余（mRMR）。为了测量四个不同的监督分类器K最近邻（KNN），支持向量机（SVM），朴素贝叶斯（NB）和决策树（DT）的分类性能，已用于小型圆形蓝细胞肿瘤（SRBCT）数据集。结果表明，与mRMR性能相比，Kendall相关性在累积方面要优于其他组合。

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