Prof. Grigorios Tsoumakas
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Academic Career Length
23Years (2004-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 July 2007 in International Journal of Data Warehousing and Mining
Journal Article
Grigorios TsoumakasIKIoannis Katakis
Ioannis Katakis
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
9 September 2010 in IEEE Transactions on Knowledge and Data Engineering
Journal Article
Grigorios TsoumakasIKIoannis Katakis
Ioannis KatakisIVIoannis Vlahavas
Ioannis VlahavasA simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.
1 January 2009
Book Chapter
Grigorios TsoumakasIKIoannis Katakis
Ioannis KatakisIVIoannis Vlahavas
Ioannis Vlahavas7 September 2007
Book Chapter
Grigorios TsoumakasIVIoannis Vlahavas
Ioannis Vlahavas19 February 2016 in Machine Learning
Journal Article
ESEleftherios Spyromitros-Xioufis
Eleftherios Spyromitros-XioufisGrigorios TsoumakasWGWilliam Groves
William GrovesIVIoannis Vlahavas
Ioannis Vlahavas1 January 2011
Book Chapter
KSKonstantinos Sechidis
Konstantinos SechidisGrigorios TsoumakasIVIoannis Vlahavas
Ioannis VlahavasAbout Me
Dr. Grigorios Tsoumakas received a degree in Computer Science from the Aristotle University of Thessaloniki (AUTH), Greece, in 1999, an MSc in Artificial Intelligence from the University of Edinburgh, United Kingdom, in 2000 and a PhD in Computer Science from AUTH in 2005. He is a Professor of Machine Learning and Knowledge Discovery at the School of Informatics of AUTH since 2024, where he has also served as Associate Professor (2020-2024), Assistant Professor (2013 – 2020) and Lecturer (2007 – 2013). Since 2024, he also serves as an Affiliate Researcher at Archimedes/RC Athena, Greece. In addition, he is co-founder and chief scientific officer at Medoid AI, a spin-off company of AUTH established in 2019, developing custom AI solutions based on cutting-edge Machine Learning technology. Dr. Tsoumakas is a senior member of ACM and IEEE. His research expertise focuses on supervised learning (ensemble methods, multi-target prediction, interpretablity) and natural language processing (semantic indexing, keyphrase extraction, summarization). He has published more than 150 research papers and according to Google Scholar he has more than 19.000 citations and an h-index of 52. His honors include receiving the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 10-Year Test of Time Award in 2017 and the Marco Ramoni best paper award at the 19th International Conference on Artificial Intelligence in Medicine (AIME 2021).