Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A Visual Query Language for Relational Knowledge Discovery TITLE2:
A Visual Query Language for Relational Knowledge Discovery TITLE2:
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting time-varying relationships in statistical relational models
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Temporal-Relational Classifiers for Prediction in Evolving Domains
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Genetic programming for protein related text classification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An evolutionary approach to constructive induction for link discovery
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Time-Evolving relational classification and ensemble methods
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Data Mining and Knowledge Discovery
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Almost all networks in real world evolve over time, and analysis of these temporal changes may help in understanding or explanation of some properties or processes of a network. This paper presents GA-TVRC, a novel Relational Time Varying Classifier which uses Genetic Algorithms to extract temporal information. GA-TVRC uses Evolutionary Strategies to optimize the influence of each previous time period on classification of new nodes. A Relational Bayesian Classifier (RBC) that is proposed by Neville et.al. [3] is utilized to compute the fitness function. The performance of GA-TVRC is compared with both the RBC, which ignores the time effect and the time varying relational classifier (TVRC) that is proposed by Sharan and Neville [20]. TVRC improves the RBC by taking the time effect into account using different predetermined weights. According to the experiments on two real world datasets, GA-TVRC extracts time effect better than the previous methods and improves the classification performance by up to 5% compared to TVRC and up to 10% compared to RBC.