改进LPU用于蛋白质功能预测

摘要

This paper formulates the protein function prediction into a typical LPU. Aiming at imbalance or over-fitting from LPU with few positive examples, it proposes a method creating synthetic examples to enlarge the set of positive examples based on the nearest neighbor and convex combination, and meanwhile modifies the procedure learning optimal classifier for the classic LPU algorithm by using one-class SVM(support vector machine) to identify the most probable negative examples, running iteratively SVM to move the classification hyperplane to a suitable place and obtaining representative negative examples through cross validation. For the yeast genomic data, the experiments show that our algorithm outperforms several classic prediction methods, particularly, for function classes with few positive examples.%本文将蛋白质功能预测定义为典型的LPU问题.针对有很少正例的LPU算法存在的不平衡或过拟合问题,提出了基于最近邻和凸组合理论的创建人工正例扩充正例集合的方法,同时使用一类支持向量机获取初始最可能的负例,通过迭代两类支持向量机将分类超平面移到一个合适的位置,由交叉验证获得代表性的负例,从而改进了典型LPU算法学习最优分类器的过程.针对酵母基因组数据的实验表明:我们的算法在很少正例的功能类上的预测性能有显著提高,在其他类上的性能也有一定的改善.