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A microarray gene expressions with classification using extreme learning machine

机译:使用极限学习机进行分类的微阵列基因表达

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In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.
机译:在当前情况下,危险疾病之一是癌症。它通过血液或淋巴扩散到身体的其他部位,它是一组细胞,显示不受控制的生长,攻击和破坏附近的组织,并偶尔转移。在癌症诊断和分子生物学中,一种有效的工具是DNA芯片。公认该技术的优势,因此关于适当检查微阵列数据出现了一些公开的疑问。在医学领域,多类别癌症分类起着非常重要的作用。由于癌症患者的数量正在增加,因此对癌症分类的需求变得至关重要。在这项研究工作中,为了克服多类别癌症分类的问题,使用了一种改进的极限学习机(ELM)分类器。它纠正了迭代学习方法所面临的问题,例如局部极小值,学习率不正确和过度拟合,并且培训快速完成。

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