用遗传算法优化基因选择，提升癌症分类准确率
An Evolutional Neural Network Framework for Classification of Microarray Data
- 结合遗传算法与多层感知机，先降维再分类
- 在多个数据集上实现超90%准确率，仅选少量关键基因
- 适合生物医学领域高维基因数据的精准分类
DNA微阵列基因表达数据广泛用于识别癌症基因标志。微阵列可提高癌症诊断与预后的准确性，但其海量基因表达数据给当前机器学习研究带来挑战。其中主要难点在于基因表达的高维度导致分类准确率下降。本研究提出一种遗传算法（GA）与多层感知机神经网络（MLP）的混合模型，通过遗传算法在特征选择阶段降低维度，随后由MLP对筛选出的基因进行分类。性能评估基于分类准确率和所选基因数量。实验结果表明，该方法相较其他机器学习算法，在多个数据集上均实现了超过90%的分类准确率，同时仅需极少数量的基因即可达成最优分类效果。
原文摘要 · Abstract (English)
DNA microarray gene-expression data has been widely used to identify cancerous gene signatures. Microarray can increase the accuracy of cancer diagnosis and prognosis. However, analyzing the large amount of gene expression data from microarray chips pose a challenge for current machine learning researches. One of the challenges lie within classification of healthy and cancerous tissues is high dimensionality of gene expressions. High dimensionality decreases the accuracy of the classification. This research aims to apply a hybrid model of Genetic Algorithm and Neural Network to overcome the problem during subset selection of informative genes. Whereby, a Genetic Algorithm (GA) reduced dimensionality during feature selection and then a Multi-Layer perceptron Neural Network (MLP) is applied to classify selected genes. The performance evaluated by considering to the accuracy and the number of selected genes. Experimental results show the proposed method suggested high accuracy and minimum number of selected genes in comparison with other machine learning algorithms.
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