Publications

Sophie Sun, Konstantinos Sechidis, Yao Chen, Jiarui Lu, Chong Ma, Ardalan Mirshani, David Ohlssen, Marc Vandemeulebroecke, Björn Bornkamp (2022). Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials. Biometrical Journal.

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Lasse Hansen, Yan-Ping Zhang, Detlef Wolf, Konstantinos Sechidis, Nicolai Ladegaard, Riccardo Fusaroli (2022). A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission. Acta Psychiatrica Scandinavica.

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Konstantinos Sechidis, Matthias Kormaksson, David Ohlssen (2021). Using knockoffs for controlled predictive biomarker identification. Statistics in Medicine, volume 40(25), pages 5453–5473.

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Konstantinos Sechidis, Riccardo Fusaroli, Juan Rafael Orozco-Arroya, Detlef Wolf, Yan-Ping Zhang (2021). A machine learning perspective on the emotional content of Parkinsonian speech. Artificial Intelligence in Medicine Volume 115, May 2021, 102061.

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Laura Morán-Fernández, Eva Blanco-Mallo, Konstantinos Sechidis, Amparo Alonso-Betanzos, Verónica Bolón-Canedo (2020). When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information. International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM).

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Laura Morán-Fernández, Konstantinos Sechidis, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Gavin Brown (2020). Feature selection with limited bit depth mutual information for portable embedded systems. Knowledge-Based Systems, volume 19, 105885.

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Eleftherios Spyromitros-Xioufis, Konstantinos Sechidis, Ioannis Vlahavas (2020). Multi-target regression via output space quantization. International Joint Conference on Neural Networks (IJCNN).

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Konstantinos Sechidis, Laura Azzimonti, Adam Pocock, Giorgio Corani, James Weatherall, Gavin Brown (2019). Efficient feature selection using shrinkage estimators. Machine Learning Journal (MLJ), volume 108(8-9), pages 1261–1286.

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Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown (2019). On the Stability of Feature Selection in the Presence of Feature Correlations. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD). Acceptance rate 130/734 (18%).

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Konstantinos Sechidis, Eleftherios Spyromitros-Xioufis, Ioannis Vlahavas (2019). Information Theoretic Multi-Target Feature Selection via Output Space Quantization. Entropy, volume 21(9).

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Veronica Bolon-Canedo, Konstantinos Sechidis, Noelia Sanchez-Marono, Amparo Alonso-Betanzos, Gavin Brown (2019). Insights into distributed feature ranking. Information Sciences, Volume 496, Pages 378-398.

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Konstantinos Sechidis, Eleftherios Spyromitros-Xioufis, Ioannis Vlahavas (2019). Multi-target feature selection through output space clustering. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).

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Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown (2018). Toward an Understanding of Adversarial Examples in Clinical Trials. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD). Acceptance rate 92/354 (26%).

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Konstantinos Sechidis, Konstantinos Papangelou, Paul Metcalfe, David Svensson, James Weatherall, Gavin Brown (2018). Distinguishing Prognostic and Predictive Biomarkers: An Information Theoretic Approach. Bioinformatics, volume 34(19), pages 3365–3376.

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Sarah Nogueira, Konstantinos Sechidis, Gavin Brown (2018). On the Stability of Feature Selection Algorithms. Journal of Machine Learning Research (JMLR), volume 18(174), pages 1-54.

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Konstantinos Sechidis, Gavin Brown (2018). Simple strategies for semi-supervised feature selection. Machine Learning Journal (MLJ), volume 107(2), pages 357-395.

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Sarah Nogueira, Konstantinos Sechidis, Gavin Brown (2017). On the Use of Spearman's Rho to Measure the Stability of Feature Rankings. Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA).

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Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Mikel Luján, Gavin Brown (2017). Dealing with under-reported variables: An information theoretic solution. International Journal of Approximate Reasoning, volume 85, pages 159-177.

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Veronica Bolon-Canedo, Konstantinos Sechidis, Noelia Sanchez-Marono, Amparo Alonso-Betanzos, Gavin Brown (2017). Exploring the consequences of distributed feature selection in DNA microarray data. International Joint Conference on Neural Networks (IJCNN).

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Konstantinos Sechidis, Emily Turner, Paul Metcalfe, James Weatherall, Gavin Brown (2017). Disentangling Prognostic and Predictive Biomarkers Through Mutual Information. Informatics for Health (I4H).

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Veronica Bolon-Canedo, Beatriz Remeseiro, Konstantinos Sechidis, David Martinez-Rego, Amparo Alonso-Betanzos (2017). Algorithmic challenges in Big Data analytics. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).

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Konstantinos Sechidis, Emily Turner, Paul Metcalfe, James Weatherall, Gavin Brown (2016). Ranking Biomarkers Through Mutual Information. NIPS Workshop on Machine Learning for Health (ML4HC).

arXiv

Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown (2016). Estimating mutual information in under-reported variables. International Conference on Probabilistic Graphical Models (PGM).

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Konstantinos Sechidis, Gavin Brown (2015). Markov blanket discovery in positive-unlabelled and semi-supervised data. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD). Acceptance rate 89/383 (23.2%).

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Konstantinos Sechidis (2015). Hypothesis Testing and Feature Selection in Semi-supervised Data. PhD Thesis, School of Computer Science, University of Manchester, UK.
PhD supervisor: Professor Gavin Brown.
AWARD Best PhD Thesis Prize of the School of Computer Science at the University of Manchester 2016 (sponsored by IBM).

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Konstantinos Sechidis, Borja Calvo, Gavin Brown (2014). Statistical Hypothesis Testing in Positive Unlabelled Data. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD). Acceptance rate 115/483 (23.8%).
AWARD Best Student Paper Award in ECML/PKDD 2014 (sponsored by Springer).
AWARD Runner up Best Paper Prize of the School of Computer Science at the University of Manchester 2014 (sponsored by IBM).

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Konstantinos Sechidis, Grigorios Tsoumakas, Ioannis Vlahavas (2014). Information theoretic feature selection in multi-label data through composite likelihood. Structural, Syntactic, and Statistical Pattern Recognition (SSPR).

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Konstantinos Sechidis, Grigorios Tsoumakas, Ioannis Vlahavas (2011). On the Stratification of Multi-label Data. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD). Acceptance rate 121/599 (20%).

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Eleftherios Spyromitros-Xioufis, Konstantinos Sechidis, Grigorios Tsoumakas, Ioannis Vlahavas (2011). MLKDs Participation at the CLEF 2011 Photo Annotation and Concept-Based Retrieval Tasks. ImageClef Lab of CLEF Conference on Multilingual and Multimodal Information Access Evaluation.

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Konstantinos Sechidis (2011). Multi-label machine learning algorithms for automated image annotation. MSc Thesis, School of Informatics, Aristotle University of Thessaloniki, Greece.
Thesis supervisor: Assistant Professor Grigorios Tsoumakas..

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