MFE-HC:基于最大特征消除技术的癌症分子模式发现混合分类器

I. Julie, E. Kirubakaran
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引用次数: 1

摘要

基因表达分析中最重要的应用是利用已知的组织样本发现或分类未知的组织样本。最近提出了几个数据挖掘分类器来预测/识别癌症模式。在本研究中,我们重点研究了支持向量机(SVM)、最近邻分类器(k-NN)、ICS4、非平行平面近端分类器(NPPC)、NPPC-SVM和基于边缘的特征消除支持向量机(MFE-SVM)等几种分类技术。从阈值水平、执行时间、内存使用和内存利用率等方面分析了这些分类器的性能。实验结果表明,不同分类器预测癌症模式的阈值水平和执行时间是不同的。我们的实验结果表明,在上述识别的分类器中,k-NN分类器预测癌症模式的执行时间较少,但消耗的执行时间较多,而MFE-SVM预测癌症模式的执行时间较少,但仍然需要更多的阈值来预测模式。即根据阈值和执行时间找到最佳的单一分类器仍然是复杂的。为了解决这个主要问题,我们提出了一种高效的分类器,称为基于最大特征消除技术的混合分类器(MFE-HC),它是k-NN和SVM分类器的组合。从结果来看,我们提出的工作在阈值和执行时间方面优于k-NN和MFE-SVM分类器。
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MFE-HC: The maximizing feature elimination technique based hybrid classifier for cancer molecular pattern discovery
The most important application of Microarray for gene expression analysis is used to discover or classify the unknown tissue samples with the help of known tissue samples. Several Data Mining Classifiers have been proposed recently to predict/identify the cancer patterns. In this research work, we have focused and studied a few Classification Techniques such as Support Vector Machine (SVM), Nearest Neighbor Classifier (k-NN), ICS4, Non-Parallel Plane Proximal Classifier (NPPC), NPPC-SVM, and Margin-based Feature Elimination-SVM (MFE-SVM). The performances of these classifiers have been analyzed in terms of Threshold Level, Execution Time, Memory Usage and Memory Utilization. From our experimental results, we revealed that the Threshold level and Execution Time to predict the Cancer Patterns are different for different Classifiers. Our experimental results established that among the above identified classifiers, the k-NN classifier achieves less Threshold to predict the cancer pattern, but however it consumes more execution time, whereas the MFE-SVM achieves less execution time to predict the cancer pattern, but it still needs more threshold to predict the Pattern. That is to find the best single classifier in terms of Threshold and Execution Time is still complicated. To address this major issue, we have proposed an efficient Classifier called Maximizing Feature Elimination Technique based Hybrid Classifier (MFE-HC), which is the combination of both k-NN and SVM classifiers. From the results, it is established that our proposed work performs better than both the k-NN and MFE-SVM Classifiers interms of Threshold and Execution Time.
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