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Eva Garcia Martin

Data Scientist | Machine Learning Researcher

Publications

Google Scholar

Journals

  • García-Martín E., Bifet A., & Lavesson N. (2019) “Energy Modeling of Hoeffding Tree Ensembles”. Intelligent Data Analysis, 2019 PDF.

  • García-Martín E., Lavesson N., Grahn H., Casalicchio E., & Boeva V. (2019). “Energy-Aware Very Fast Decision Tree”. Journal of Data Science and Analytics (JDSA). Springer. [Springer] (https://link.springer.com/article/10.1007/s41060-021-00246-4)

  • García-Martín E., Rodrigues C., Riley G., & Grahn H. (2019) “Estimation of Energy Consumption in Machine Learning”. Journal of Parallel and Distributed Computing, 2019, Elsevier. DOI: https://doi.org/10.1016/j.jpdc.2019.07.007 - ScienceDirect

  • García-Martín E., Lavesson N., & Doroud M. (2016). “Hashtags and followers:An experimental study of the online social network Twitter”. Social Network Analysis and Mining (SNAM), 6(1) (pp. 1-15), Springer original PDF

  • Abghari, S., Garcia-Martin, E., Johansson, C., Lavesson, N., & Grahn, H. (2017). “Trend analysis to automatically identify heat program changes”. Energy Procedia, 116, 407-415.

Book Chapters

  • García-Martín E., Lavesson N., & Grahn H. (2017). “Energy Efficiency Analysis of the Very Fast Decision Tree algorithm”. In: * Missaoui R., Abdessalem T., Latapy M. (eds) Trends in Social Network Analysis. Lecture Notes in Social Networks, (pp. 229-252), Springer.PDF

Conference papers

  • García-Martín E., Lavesson N., Grahn H., Casalicchio E., & Boeva V. (2018) “Hoeffding Trees with nmin adaptation”. In 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 70-79) IEE arXiv

  • García-Martín E., Lavesson N., & Grahn H. (2017). “Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree”. In: Au M., Castiglione A., Choo KK., Palmieri F., Li KC. (eds) Green, Pervasive, and Cloud Computing. GPC 2017. Lecture Notes in Computer Science, 10232, (pp. 267-281), Springer. PDF

  • Lundberg L., Lennerstad H., García-Martín E., Lavesson N., Boeva V. (2017) “Increasing the Margin in Support Vector Machines through Hyperplane Folding”, 26th Annual Machine Learning Conference of the Benelux (Benelearn).

  • García-Martín E., Lavesson N., & Grahn H. (2015) Energy Efficiency in Data Stream Mining. Advances in Social Networks Analysis and Mining (ASONAM), 2015 IEEE/ACM International Conference on. IEEE, 2015. ACM

Workshop papers

  • García-Martín E., Lavesson N., Grahn H., Casalicchio E., & Boeva V. (2018). “How to Measure Energy Consumption in Machine Learning Algorithms”. ECML-PKDD 2018 1st International Workshop on Energy Efficient Data Mining and Knowledge Discovery (Green Data Mining)

  • Garcia-Martin E., Lavesson N., Grahn H., & Boeva V. (2017). “Energy Efficiency in Machine Learning: A position paper”. In 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden 137, (pp. 68-72). Linköping University Electronic Press. PDF

  • García-Martín E., & Lavesson N., “Is it ethical to avoid error analysis?” 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017), held in conjunction with the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, arxiv

  • García-Martín E., Lavesson N., Grahn H., Casalicchio E., & Boeva V. “Adaptive Very Fast Decision Tree, preliminary results,” in 12th Women in Machine Learning Workshop (WiML 2017), in conjunction with NIPS 2017, December 2017, Long Beach, USA. Presented as a poster. Poster

  • García-Martín E., Lavesson N., Grahn H., Casalicchio E., & Boeva V., “Adaptive Very Fast Decision Tree, preliminary results,” in 5th Swedish Workshop in Data Science (SweDS 2017), December 2017, Gothenburg, Sweden.

  • García-Martín E., Lavesson N., & Grahn H, “Energy Efficiency in Machine Learning”. 4th Swedish Workshop on Data Science (SweDS 2016).

Thesis work

  • García-Martín, Eva. Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining. Diss. Blekinge Tekniska Högskola, 2019. PDF

  • García-Martín, Eva. Extraction and Energy Efficient Processing of Streaming Data. Diss. Blekinge Tekniska Högskola, 2017. PDF