(aside image)

Emil Eirola

D.Sc. (Tech.), Researcher

Office:
Room A344 in Computer Science Building,
Konemiehentie 2, Otaniemi campus area, Espoo
Postal Address:
Aalto University
Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
Telephone:
+358 50 4302878
Email:
firstname.lastname at aalto.fi

Description

Since defending my doctoral degree in 2014, I work as a researcher at the Department of Business Management and Analytics at Arcada University of Applied Sciences. I am part of the CloSe project on machine learning applications in computer security for cloud services.

Research

My focus is on large scale data science applications in computer security and finance, with a particular interest in machine learning with incomplete data (missing values). Previously also work on noise variance estimation and input selection methods, particularly from the point of view of autoregressive time series prediction.

Publications

List of Publications. Order by:   Type | Date


2016

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


3. Android Malware Detection: Building Useful Representationspdf filebibtex file

Luiza Sayfullina, Emil Eirola, Dmitry Komashinsky, Paolo Palumbo and Juha Karhunen.
   In IEEE 15th International Conference on Machine Learning and Applications. 2016.

2. Probabilistic Methods for Multiclass Classification Problemsdoibibtex file

Andrey Gritsenko, Emil Eirola, Daniel Schupp, Edward Ratner and Amaury Lendasse.
   In Proceedings of ELM-2015 Volume 2, volume 7, pages 385-397. 2016.

1. Extreme Learning Machine for Missing Data using Multiple Imputationsdoipdf filebibtex file

Dušan Sovilj, Emil Eirola, Yoan Miche, Kaj-Mikael Bjork, Rui Nian, Anton Akusok and Amaury Lendasse.
   In Neurocomputing, volume 174, Part A, pages 220 - 231. 2016.

2015

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


3. Efficient detection of zero-day Android Malware using Normalized Bernoulli Naive Bayesdoipdf filebibtex file

Luiza Sayfullina, Emil Eirola, Dmitry Komashinsky, Paolo Palumbo, Yoan Miche, Amaury Lendasse and Juha Karhunen.
   In Trustcom/BigDataSE/ISPA, 2015 IEEE, volume 1, pages 198-205. 2015.

2. Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Modelsdoipdf filebibtex file

Emil Eirola, Andrey Gritsenko, Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Dušan Sovilj, Rui Nian, Bo He and Amaury Lendasse.
   In Advances in Computational Intelligence - 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II, volume 9095, pages 153-164. 2015.

1. Reducing sparsity for Text Classificationbibtex file

Luiza Sayfullina, Yoan Miche and Emil Eirola.
   In International Conference on Computational Social Science. 2015, Accepted..

2014

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


5. Machine learning methods for incomplete data and variable selectionurlbibtex file

Emil Eirola.
October, 2014.

4. Variable Selection for Regression Problems Using Gaussian Mixture Models to Estimate Mutual Informationdoipdf filebibtex file

Emil Eirola, Amaury Lendasse and Juha Karhunen.
   In International Joint Conference on Neural Networks (IJCNN 2014), pages 1606-1613. July, 2014.

3. The Delta Test: The 1-NN Estimator as a Feature Selection Criteriondoipdf filebibtex file

Emil Eirola, Amaury Lendasse, Francesco Corona and Michel Verleysen.
   In International Joint Conference on Neural Networks (IJCNN 2014), pages 4214-4222. July, 2014.

2. Forecasting the Outbursts of the Photometry Light Curve of Star V363 Lyrpdf filebibtex file

Alexander Grigorievskiy, Maarit Mantere, Anton Akusok, Emil Eirola and Amaury Lendasse.
   In International Work Conference on Time Series Analysis, volume 1, pages 520-531. 2014.

1. Mixture of Gaussians for distance estimation with missing datadoipdf filebibtex file

Emil Eirola, Amaury Lendasse, Vincent Vandewalle and Christophe Biernacki.
   In Neurocomputing, volume 131, pages 32--42. 2014.

2013

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


3. Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolationdoipdf filebibtex file

Emil Eirola and Amaury Lendasse.
   In Advances in Intelligent Data Analysis XII 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, volume 8207, pages 162-173. 2013.

2. Distance Estimation in Numerical Data Sets with Missing Valuesdoipdf filebibtex file

Emil Eirola, Gauthier Doquire, Michel Verleysen and Amaury Lendasse.
   In Information Sciences, volume 240, pages 115--128. 2013.

1. Regularized Extreme Learning Machine For Regression with Missing Datadoipdf filebibtex file

Qi Yu, Yoan Miche, Emil Eirola, Mark van Heeswijk, Eric Séverin and Amaury Lendasse.
   In Neurocomputing, volume 102, pages 45–51. 2013.

2012

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


1. Mixture of Gaussians for Distance Estimation with Missing Dataurlbibtex file

Emil Eirola, Amaury Lendasse, Vincent Vandewalle and Christophe Biernacki.
   In Machine Learning Reports 03/2012, pages 37-45. 2012, Proceedings of the Workshop - New Challenges in Neural Computation 2012.

2011

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


1. Ensembles of Local Linear Models for Bankruptcy Analysis and Predictionurlpdf filebibtex file

Laura Kainulainen, Yoan Miche, Emil Eirola, Qi Yu, Benoît Frénay, Eric Séverin and Amaury Lendasse.
   In Case Studies in Business, Industry and Government Statistics (CSBIGS), volume 4. November, 2011.

2010

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


2. Ensembles of Locally Linear Models: Application to Bankruptcy Predictionpdf filebibtex file

Laura Kainulainen, Qi Yu, Yoan Miche, Emil Eirola, Eric Séverin and Amaury Lendasse.
   In Proceedings of the 2010 International Conference on Data Mining, pages 280--286. July, 2010.

1. Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputspdf filebibtex file

Yoan Miche, Emil Eirola, Patrick Bas, Olli Simula, Christian Jutten, Amaury Lendasse and Michel Verleysen.
   In ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 19--24. April 28--30, 2010.

2009

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


1. Variable Selection with the Delta Test in Theory and Practicepdf filebibtex file

Emil Eirola.
November, 2009, Master's thesis.

2008

2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008


1. Using the Delta test for variable selection pdf filebibtex file

Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona and Michel Verleysen.
   In Proceedings of ESANN 2008, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 25--30. April 23-25, 2008.



Material on this web site is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders, notwithstanding that they have offered their works here electronically. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder. Copyright holders claiming that the material available below is not in accordance with copyright terms and constraints are invited to contact the author by e-mail and ask him to remove the links to specific manuscripts. Most PDF files of journal articles and book chapters contain a non-final version of the manuscript (but still, sufficiently close to it...).