Publications

2024

  • Sechidis K, Sun S, Chen Y, Lu J, Zhang C, Baillie M, Ohlssen D, Vandemeulebroecke M, Hemmings R, Ruberg S & Bornkamp B.
    WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors
    arXiv. 2024. doi:10.48550/arXiv.2405.00859

  • Zimmermann MR, Baillie M, Kormaksson M, Ohlssen D & Sechidis K.
    All that Glitters Is not Gold: Type I Error Controlled Variable Selection from Clinical Trial Data.
    Clinical Pharmacology and Therapeutics. 2024. doi:10.1002/cpt.3211

  • Sun S, Sechidis K, Chen Y, Lu J, Ma C, Mirshani A, Ohlssen D, Vandemeulebroecke M, & Bornkamp B.
    Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials.
    Biometrical Journal. 2024; 66, 2100337. doi:10.1002/bimj.202100337

2022

  • Hansen L, Zhang Y-P, Wolf D, Sechidis K, Ladegaard N, Fusaroli R.
    A generalizable speech emotion recognition model reveals depression and remission.
    Acta Psychiatrica Scandinavica. 2022; 145: 186–199. doi:10.1111/acps.13388

2021

  • Sechidis K, Kormaksson M, Ohlssen D.
    Using knockoffs for controlled predictive biomarker identification.
    Statistics in Medicine. 2021; 40(25): 5453–5473. doi:10.1002/sim.9134, code

  • Sechidis K, Fusaroli R, Orozco-Arroyave JR, Wolf D, Zhang YP.
    A machine learning perspective on the emotional content of Parkinsonian speech.
    Artificial Intelligence in Medicine. 2021; 115:102061. doi:10.1016/j.artmed.2021.102061

2020

  • Moran-Fernandez L, Sechidis K, Bolon-Canedo V, Alonso-Betanzos A, Brown G.
    Feature selection with limited bit depth mutual information for portable embedded systems.
    Knowledge-Based Systems. 2020; 197:105885. doi:10.1016/j.knosys.2020.105885

  • Spyromitros-Xioufis E, Sechidis K, Vlahavas I.
    Multi-target regression via output space quantization.
    International Joint Conference on Neural Networks (IJCNN). 2020. IEEE. doi:10.1109/IJCNN48605.2020.9206984

  • Morán-Fernández L, Blanco-Mallo E, Sechidis K, Alonso-Betanzos A, Bolón-Canedo V.
    When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information.
    ECML/PKDD 2020 ITEM Workshop: Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. doi:10.1007/978-3-030-66770-2_18

  • Nikolaou N, Sechidis K.
    Inferring Causal Direction from Observational Data: A Complexity Approach.
    ECML/PKDD 2020 PharML Workshop: Machine Learning for Pharma and Healthcare Applications. doi:10.48550/arXiv.2010.05635

2019

  • Sechidis K, Azzimonti L, Pocock A, Corani G, Weatherall J, Brown G.
    Efficient feature selection using shrinkage estimators.
    Machine Learning Journal (MLJ). 2019; 108: 1261–1286. doi:10.1007/s10994-019-05795-1, code

  • Sechidis K, Spyromitros-Xioufis E, Vlahavas I.
    Information Theoretic Multi-Target Feature Selection via Output Space Quantization.
    Entropy. 2019; 21(9):855. doi:10.3390/e21090855, code

  • Bolon-Canedo V, Sechidis K, Sanchez-Marono N, Alonso-Betanzos A,Brown G.
    Insights into distributed feature ranking.
    Information Sciences. 2019;496:378-398. doi:10.1016/j.ins.2018.09.045

  • Sechidis K, Papangelou K, Nogueira S, Weatherall J, Brown G.
    On the Stability of Feature Selection in the Presence of Feature Correlations.
    Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD) 2019. doi:10.1007/978-3-030-46150-8_20., code
    Acceptance rate 130/734 (18%)

  • Sechidis K, Spyromitros-Xioufis E, Vlahavas I.
    Multi-target feature selection through output space clustering.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2019. paper

2018

  • Sechidis K, Papangelou K, Metcalfe PD, Svensson D, Weatherall J, Brown G.
    Distinguishing prognostic and predictive biomarkers: an information theoretic approach.
    Bioinformatics, 2018; 34(19): 3365–3376. doi:10.1093/bioinformatics/bty357, code

  • Sechidis K, Brown G.
    Simple strategies for semi-supervised feature selection.
    Machine Learning Journal (MLJ). 2018; 107(2): 357-395. doi:10.1007/s10994-017-5648-2, code

  • Nogueira S, Sechidis K, Brown G.
    On the Stability of Feature Selection Algorithms.
    Journal of Machine Learning Research (JMLR).2018; 18(174):1-54. paper

  • Papangelou K, Sechidis K, Weatherall J, Brown G.
    Toward an Understanding of Adversarial Examples in Clinical Trials.
    Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD) 2018. doi:10.1007/978-3-030-10925-7_3.
    Acceptance rate 92/354 (26%)

2017

  • Sechidis K, Sperrin M., Petherick ES, Luján M., Brown, G.
    Dealing with under-reported variables: An information theoretic solution.
    International Journal of Approximate Reasoning, 2017. 8;159-177. doi:10.1016/j.ijar.2017.04.002, code

  • Bolon-Canedo V, Sechidis K, Sanchez-Marono N, Alonso-Betanzos A, Brown G.
    Exploring the consequences of distributed feature selection in DNA microarray data.
    International Joint Conference on Neural Networks (IJCNN). 2017. IEEE. doi:10.1109/IJCNN.2017.7966051

  • Sechidis K, Turner E, Metcalfe PD, Svensson D, Weatherall J, Brown G.
    Disentangling Prognostic and Predictive Biomarkers Through Mutual Information.
    Informatics for Health (I4H). 2017. doi:10.3233/978-1-61499-753-5-141

  • Nogueira S, Sechidis K, Brown G.
    On the Use of Spearman’s Rho to Measure the Stability of Feature Rankings.
    Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA) 2017. doi:10.1007/978-3-319-58838-4_42

  • Bolon-Canedo V, Remeseiro B, Sechidis K, Martinez-Rego D, Alonso-Betanzos A.
    Algorithmic challenges in Big Data analytics.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2017. paper

2016

  • Sechidis K, Sperrin M., Petherick ES, Luján M., Brown, G.
    Estimating mutual information in under-reported variables.
    International Conference on Probabilistic Graphical Models (PGM) 2016. paper

  • Sechidis K, Turner E, Metcalfe PD, Svensson D, Weatherall J, Brown G.
    Ranking Biomarkers Through Mutual Information.
    NeurIPS Workshop on Machine Learning for Health (ML4HC) 2016. doi:10.48550/arXiv.1612.01316

2015

2014

  • Sechidis K, Calvo B, Brown G.
    Statistical Hypothesis Testing in Positive Unlabelled Data.
    Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD) 2014. doi:10.1007/978-3-662-44845-8_5.
    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).

  • Sechidis K, Tsoumakas G, Vlahavas I. Information theoretic feature selection in multi-label data through composite likelihood. Structural, Syntactic, and Statistical Pattern Recognition (SSPR) 2014. doi:10.1007/978-3-662-44415-3_15

2011

  • Sechidis K, Tsoumakas G, Vlahavas I.
    On the Stratification of Multi-label Data.
    Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD) 2011. doi:10.1007/978-3-642-23808-6_10.
    Acceptance rate 121/599 (20%).

  • Spyromitros-Xioufis E, Sechidis K, Tsoumakas G, Vlahavas I.
    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. paper

  • Sechidis K.
    Multi-label machine learning algorithms for automated image annotation.
    MSc Thesis, School of Informatics, Aristotle University of Thessaloniki, Greece. thesis.
    Thesis supervisor: Professor Grigorios Tsoumakas.