基于PageRank的主动学习算法

在许多分类任务中，存在大量未标记的样本，并且获取样本标签耗时且昂贵。利用主动学习算法确定最应被标记的关键样本，来构建高精度分类器，可以最大限度地减少标记成本。本文提出一种基于PageRank的主动学习算法（PAL），充分利用数据分布信息进行有效的样本选择。利用PageRank根据样本间的相似度关系依次计算邻域、分值矩阵和排名向量；选择代表样本，并根据其相似度关系构建二叉树，利用该二叉树对代表样本进行聚类，标记和预测；将代表样本作为训练集，对其他样本进行分类。实验采用8个公开数据集，与5种传统的分类算法和3种流行的主动学习算法比较，结果表明PAL算法能取得更好的分类效果。

Active learning through PageRank

In many classification tasks, there are a large number of unlabeled samples, and it is expensive and time-consuming to obtain a label for each class. The goal of active learning is to train an accurate classifier with minimum cost by labeling the most informative samples. In this paper, we propose a PageRank-based active learning algorithm (PAL), which makes full use of sample distribution information for effective sample selection. First, based on the PageRank theory, we sequentially calculate the neighborhoods, score matrices, and ranking vectors based on similarity relationships in the data. Next, we select representative samples and establish a binary tree to express the relationships between representative samples. Then, we use a binary tree to cluster, label, and predict representative samples. Lastly, we regard the representative samples as training sets for classifying other samples. We conducted experiments on eight datasets to compare the performance of our proposed algorithm with those of five traditional classification algorithms and three state-of-the-art active learning algorithms. The results demonstrate that PAL obtained higher classification accuracy.