School of Computer Science, University of Windsor, 5115 Lambton Tower, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada.
IEEE/ACM Trans Comput Biol Bioinform. 2013 Mar-Apr;10(2):447-56. doi: 10.1109/TCBB.2013.30.
Microarray data can be used to detect diseases and predict responses to therapies through classification models. However, the high dimensionality and low sample size of such data result in many computational problems such as reduced prediction accuracy and slow classification speed. In this paper, we propose a novel family of nonnegative least-squares classifiers for high-dimensional microarray gene expression and comparative genomic hybridization data. Our approaches are based on combining the advantages of using local learning, transductive learning, and ensemble learning, for better prediction performance. To study the performances of our methods, we performed computational experiments on 17 well-known data sets with diverse characteristics. We have also performed statistical comparisons with many classification techniques including the well-performing SVM approach and two related but recent methods proposed in literature. Experimental results show that our approaches are faster and achieve generally a better prediction performance over compared methods.
微阵列数据可通过分类模型用于检测疾病和预测治疗反应。然而，此类数据的高维性和低样本量导致了许多计算问题，例如预测精度降低和分类速度慢。在本文中，我们提出了一类新的用于高维微阵列基因表达和比较基因组杂交数据的非负最小二乘分类器。我们的方法基于结合使用局部学习、转导学习和集成学习的优势，以获得更好的预测性能。为了研究我们方法的性能，我们在具有不同特征的 17 个著名数据集上进行了计算实验。我们还与许多分类技术进行了统计比较，包括性能良好的 SVM 方法和文献中提出的两个相关但最近的方法。实验结果表明，与比较方法相比，我们的方法速度更快，通常具有更好的预测性能。
