Emil Eirola, PhD

Biography

Emil Eirola is a Post-doctoral Research Scientist at the Department of Business Management and Analytics at the Arcada University of Applied Sciences in Helsinki, Finland. He received his M.Sc. degree in Mathematics (2009) and D.Sc. degree in Information Science (2014) from the Aalto University School of Science. His research interests include machine learning with incomplete data, feature selection, and mixture models, with applications to finance, security, and environmental data.

Selected publications

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Sayfullina, L., Eirola, E., Komashinsky, D., Palumbo, P. & Karhunen, J. Android Malware Detection: Building Useful Representations. in IEEE 15th International Conference on Machine Learning and Applications (IEEE ICMLA 2016) (2016).
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Sovilj, D. et al. Extreme Learning Machine for Missing Data using Multiple Imputations. Neurocomputing 174, Part A, 220–231 (2016).
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Eirola, E., Akusok, A., Björk, K.-M., Johnson, H. & Lendasse, A. Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values. in Proceedings of the International Conference on Extreme Learning Machines (ELM 2016) (2016).
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Eirola, E. et al. Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. in Advances in Computational Intelligence 9095, 153–164 (LNCS, 2015).
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Eirola, E., Lendasse, A., Vandewalle, V. & Biernacki, C. Mixture of Gaussians for distance estimation with missing data. Neurocomputing 131, 32–42 (2014).
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Eirola, E., Lendasse, A., Corona, F. & Verleysen, M. The Delta Test: The 1-NN Estimator as a Feature Selection Criterion. in 4214–4222 (2014).
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Eirola, E., Lendasse, A. & Karhunen, J. Variable Selection for Regression Problems Using Gaussian Mixture Models to Estimate Mutual Information. in 1606–1613 (2014).
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Eirola, E., Doquire, G., Verleysen, M. & Lendasse, A. Distance Estimation in Numerical Data Sets with Missing Values. Information Sciences 240, 115–128 (2013).
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Eirola, E. & Lendasse, A. Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation. in 8207, 162–173 (LNCS, 2013).
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Yu, Q. et al. Regularized Extreme Learning Machine For Regression with Missing Data. Neurocomputing 102, 45–51 (2013).