Unnikrishnan, Vishnu

2020

Assessing the Difficulty of Labelling an Instance in Crowdworking.. 2nd Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning@ ECML PKDD 2020, 2020.

Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder. In Michael R. Berthold, Ad Feelders, and Georg Krempl (Eds.), Advances in Intelligent Data Analysis XVIII, 1--13, Springer International Publishing, Cham, 2020. URL

Resource management for model learning at entity level. Annals of Telecommunications, Aug 29, 2020. URL

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?. In Annalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, and Stan Matwin (Eds.), DS, (12323):659-673, Springer, 2020. URL

Active feature acquisition on data streams under feature drift. Annals of Telecommunications, Jul 8, 2020. URL

2019

Active Feature Acquistion for Opinion Stream Classification under Drift. Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2019), 108--111, 2019. URL

Exploiting Entity Information for Stream Classification over a Stream of Reviews. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 564-573, ACM, 2019. URL

Assessing the reliability of crowdsourced labels via Twitter. Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", 2019. URL

Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity. International Journal of Data Science and Analytics, 2019. URL

2018

Predicting Document Polarities on a Stream without Reading their Contents. Proceedings of the Symposium on Applied Computing (SAC), 2018.

Entity-Level Stream Classification: Exploiting Entity Similarity to Label the Future Observations Referring to an Entity. 2018.

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