图 书 馆
Cosnet：一种用于图形半监督学习的成本敏感神经网络COSNet: a Cost Sensitive Neural Network for Semi-supervised Learning in Graphs |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_frasca_cosnet/ |
| 主讲教师: | Marco Frasca |
| 开课单位: | 米兰大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The semi-supervised problem of learning node labels in graphs consists, given a partial graph labeling, in inferring the unknown labels of the unlabeled vertices. Several machine learning algorithms have been proposed for solving this problem, including Hopfield networks and label propagation methods; however, some issues have been only partially considered, e.g. the preservation of the prior knowledge and the unbalance between positive and negative labels. To address these items, we propose a Hopfield-based cost sensitive neural network algorithm (COSNet). The method factorizes the solution of the problem in two parts: 1) the subnetwork composed by the labelled vertices is considered, and the network parameters are estimated through a supervised algorithm; 2) the estimated parameters are extended to the subnetwork composed of the unlabeled vertices, and the attractor reached by the dynamics of this subnetwork allows to predict the labeling of the unlabeled vertices. The proposed method embeds in the neural algorithm the "a priori" knowledge coded in the labelled part of the graph, and separates node labels and neuron states, allowing to differentially weight positive and negative node labels. Moreover, COSNet introduces an efficient costsensitive strategy which allows to learn the near-optimal parameters of the network in order to take into account the unbalance between positive and negative node labels. Finally, the dynamics of the network is restricted to its unlabeled part, preserving the minimization of the overall objective function and significantly reducing the time complexity of the learning algorithm. COSNet has been applied to the genome-wide prediction of gene function in a model organism. The results, compared with those obtained by other semi-supervised label propagation algorithms and supervised machine learning methods, show the effectiveness of the proposed approach. |
| 关 键 词: | 半监督问题; 标签传播方法; 解决方案 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-02：cwx |
| 阅读次数: | 98 |