Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Machine Learning
Machine Learning
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Exploratory Social Network Analysis with Pajek
Exploratory Social Network Analysis with Pajek
ICML '06 Proceedings of the 23rd international conference on Machine learning
Exploiting Network Structure for Active Inference in Collective Classification
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Why Stacked Models Perform Effective Collective Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Within-Network Classification Using Local Structure Similarity
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
User position measures in social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Epidemics and Rumours in Complex Networks
Epidemics and Rumours in Complex Networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Online active inference and learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Label-dependent node classification in the network
Neurocomputing
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Social Network in the Social Recommender System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An analysis of how ensembles of collective classifiers improve predictions in graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
In relational learning tasks such as within network classification the main problem arises from the inference of nodes' labels based on the the ground true labels of remaining nodes. The problem becomes even harder if the nodes from initial network do not have any labels assigned and they have to be acquired. However, labels of which nodes should be obtained in order to provide fair classification results? Active learning and inference is a practical framework to study this problem. The method for active learning and inference in within network classification based on node selection is proposed in the paper. Based on the structure of the network it is calculated the utility score for each node, the ranking is formulated and for selected nodes the labels are acquired. The paper examines several distinct proposals for utility scores and selection methods reporting their impact on collective classification results performed on various real-world networks.