Abstract
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.
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Güneş, İ., Çataltepe, Z., Öğüdücü, Ş.G. (2011). GA-TVRC: A Novel Relational Time Varying Classifier to Extract Temporal Information Using Genetic Algorithms. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_42
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DOI: https://doi.org/10.1007/978-3-642-23199-5_42
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