TY - GEN
T1 - GA-TVRC
T2 - 7th International Conference on Machine Learning and Data Mining, MLDM 2011
AU - Güneş, Ismail
AU - Çataltepe, Zehra
AU - Öǧüdücü, Şule Gündüz
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Evolutionary Strategies
KW - Evolving Networks
KW - Genetic Algorithms
KW - Relational Bayesian Classifier
KW - Time-Varying Relational Classifier
UR - http://www.scopus.com/inward/record.url?scp=80052323691&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23199-5_42
DO - 10.1007/978-3-642-23199-5_42
M3 - Conference contribution
AN - SCOPUS:80052323691
SN - 9783642231988
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 568
EP - 583
BT - Machine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings
Y2 - 30 August 2011 through 3 September 2011
ER -