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L-SHADE-E: Ensemble of two differential evolution algorithms originating from L-SHADE.

, , , and . Inf. Sci., (2021)

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Combining heterogeneous features for time series prediction., , , , , , , and . BESC, page 1-2. IEEE, (2017)Cost-Sensitive Churn Prediction in Fund Management Services., , , , , and . DASFAA (2), volume 10828 of Lecture Notes in Computer Science, page 776-788. Springer, (2018)Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach., , , , , and . ADC, volume 10837 of Lecture Notes in Computer Science, page 123-134. Springer, (2018)Relational Autoencoder for Feature Extraction., , , and . CoRR, (2018)Discovering influential authors in heterogeneous academic networks by a co-ranking method., and . CIKM, page 1029-1036. ACM, (2013)A Multiple Source based Transfer Learning Framework for Marketing Campaigns., , , , , and . IJCNN, page 1-8. IEEE, (2018)Using network evolution theory and singular value decomposition method to improve accuracy of link prediction in social networks., and . AusDM, volume 134 of CRPIT, page 175-182. Australian Computer Society, (2012)Community detection on heterogeneous networks by multiple semantic-path clustering., , and . CASoN, page 7-12. IEEE, (2014)L-SHADE-E: Ensemble of two differential evolution algorithms originating from L-SHADE., , , and . Inf. Sci., (2021)Heterogeneous network analysis on academic collaboration networks. University of Technology Sydney, Australia, (2014)