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Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set. 相似文献
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Gene subset selection in microarray data using entropic filtering for cancer classification 总被引:1,自引:1,他引:0
Abstract: In this work an entropic filtering algorithm (EFA) for feature selection is described, as a workable method to generate a relevant subset of genes. This is a fast feature selection method based on finding feature subsets that jointly maximize the normalized multivariate conditional entropy with respect to the classification ability of tumours. The EFA is tested in combination with several machine learning algorithms on five public domain microarray data sets. It is found that this combination offers subsets yielding similar or much better accuracies than using the full set of genes. The solutions obtained are of comparable quality to previous results, but they are obtained in a maximum of half an hour computing time and use a very low number of genes. 相似文献
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针对中国地鼠基因表达谱数据维数高和样本小的特点,提出一种基于支持向量机(SVM)的分类特征基因选取方法。该方法利用改进的Fisher判别(FDR)基因特征计分准则剔除分类无关基因,提出由空间距离和功能距离组成的新距离作为相似性度量的标准进行冗余基因的剔除,采用SVM作为分类器检验特征基因的分类性能。实验结果表明,该方法有效地剔除了分类无关基因和冗余基因,选取的特征基因满足对中国地鼠正确分类的最小基因数。 相似文献
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随着大规模基因表达谱技术的发展,基于基因表达谱的癌症诊断方法正在成为临床医学上一种快速有效的诊断方法,但是由于基因表达数据维数过高、样本量小、噪声大,使得正确提取有关癌症的特征基因成为关键。以结肠癌肿瘤的基因表达谱数据为例,提出了结合Fisher权函数、离散傅里叶变换和主成分分析的混合特征基因提取方法,以多元Logistic回归分析和贝叶斯决策作为分类器进行肿瘤分类检测。实验结果表明,该方法对于结肠癌数据集CV识别准确率高达96.80%。 相似文献
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Context
Existing test suite prioritization techniques usually rely on code coverage information or historical execution data that serve as indicators for estimating the fault-detecting ability of test cases. Such indicators are primarily empirical in nature and not theoretically driven; hence, they do not necessarily provide sound estimates. Also, these techniques are not applicable when the source code is not available or when the software is tested for the first time.Objective
We propose and develop the novel notion of fault-based prioritization of test cases which directly utilizes the theoretical knowledge of their fault-detecting ability and the relationships among the test cases and the faults in the prescribed fault model, based on which the test cases are generated.Method
We demonstrate our approach of fault-based prioritization by applying it to the testing of the implementation of logical expressions against their specifications. We then validate our proposal by an empirical study that evaluates the effectiveness of prioritization techniques using two different metrics.Results
A theoretically guided fault-based prioritization technique generally outperforms other techniques under study, as assessed by two different metrics. Our empirical results also show that the technique helps to reveal all target faults by executing only about 72% of the prioritized test suite, thereby reducing the effort required in testing.Conclusions
The fault-based prioritization approach is not only applicable to the instance empirically validated in this paper, but should also be adaptable to other fault-based testing strategies. We also envisage new research directions to be opened up by our work. 相似文献30.
目前,基于混合方法的相似度计算对影响语义相似度的因素分析不全面。针对这个问题,提出了基于多个影响术语语义相似度度量因素的综合方法。该方法结合语义层次,语义距离和局部语义密度,充分运用本体的语义信息来计算基因术语间的语义相似度。实验结果表明,该方法与人工打分的相关系数更高。 相似文献