2022 - Research.com Computer Science in Greece Leader Award
Artificial intelligence, Machine learning, Multi-label classification, Data mining and Classifier are his primary areas of study. His work carried out in the field of Artificial intelligence brings together such families of science as Field and Pattern recognition. His Pruning, Ensemble learning and Support vector machine study in the realm of Machine learning connects with subjects such as Thresholding.
His work on Classifier chains as part of general Multi-label classification study is frequently connected to Disjoint sets, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Classifier chains research is multidisciplinary, incorporating elements of Theoretical computer science, Multi label learning, Single label, Latent semantic indexing and Web mining. He combines subjects such as Supervised learning and Set with his study of Data mining.
Ioannis Vlahavas mainly focuses on Artificial intelligence, Machine learning, Data mining, Information retrieval and Task. The concepts of his Artificial intelligence study are interwoven with issues in Domain and Pattern recognition. His studies deal with areas such as Statistical classification, Translation initiation sites, Feature selection and Cluster analysis as well as Data mining.
His Information retrieval study incorporates themes from Web service and Metadata. His work in Metadata addresses subjects such as RDF, which are connected to disciplines such as Knowledge base. He has included themes like Set and Regression in his Random forest study.
Ioannis Vlahavas mainly investigates Artificial intelligence, Task, Machine learning, Information retrieval and Information and Computer Science. His Artificial intelligence research includes themes of Field and Natural language processing. His Task study integrates concerns from other disciplines, such as Identification, Deep learning, Multi-label classification, Heuristics and Search engine indexing.
In his study, Data mining, Support vector machine and Association rule learning is strongly linked to Supervised learning, which falls under the umbrella field of Multi-label classification. Ioannis Vlahavas interconnects Operator and Process in the investigation of issues within Machine learning. The various areas that Ioannis Vlahavas examines in his Information retrieval study include Classifier and Convolutional neural network.
His main research concerns Information and Computer Science, Task, Supervised learning, Data mining and Ontology. The concepts of his Task study are interwoven with issues in Question answering, Information retrieval, Search engine indexing and Multi-label classification. He is investigating Machine learning and Artificial intelligence as part of his examination of Supervised learning.
Ioannis Vlahavas combines subjects such as Field and Data science with his study of Machine learning. He undertakes interdisciplinary study in the fields of Artificial intelligence and Genetic data through his research. His Data mining research is multidisciplinary, incorporating perspectives in Selection, Single-nucleotide polymorphism, Pairwise comparison and Feature selection.
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Mining Multi-label Data
Grigorios Tsoumakas;Ioannis Katakis;Ioannis P. Vlahavas.
Data Mining and Knowledge Discovery Handbook (2009)
Random k-Labelsets: An Ensemble Method for Multilabel Classification
Grigorios Tsoumakas;Ioannis Vlahavas.
european conference on machine learning (2007)
MULTI-LABEL CLASSIFICATION OF MUSIC INTO EMOTIONS
Konstantinos Trohidis;Grigorios Tsoumakas;George Kalliris;Ioannis P. Vlahavas.
international symposium/conference on music information retrieval (2008)
Random k-Labelsets for Multilabel Classification
G. Tsoumakas;I. Katakis;I. Vlahavas.
IEEE Transactions on Knowledge and Data Engineering (2011)
Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis;Olga Tsave;Athanasios Salifoglou;Nicos Maglaveras.
Computational and structural biotechnology journal (2017)
MULAN: A Java Library for Multi-Label Learning
Grigorios Tsoumakas;Eleftherios Spyromitros-Xioufis;Jozef Vilcek;Ioannis Vlahavas.
Journal of Machine Learning Research (2011)
Cultures in negotiation: teachers' acceptance/resistance attitudes considering the infusion of technology into schools
S. Demetriadis;A. Barbas;A. Molohides;G. Palaigeorgiou.
Computer Education (2003)
Multilabel Text Classification for Automated Tag Suggestion
I. Katakis;I. Vlahavas;G. Tsoumakas.
european conference on principles of data mining and knowledge discovery (2008)
An Empirical Study of Lazy Multilabel Classification Algorithms
E. Spyromitros;G. Tsoumakas;Ioannis Vlahavas.
hellenic conference on artificial intelligence (2008)
Protein classification with multiple algorithms
Sotiris Diplaris;Grigorios Tsoumakas;Pericles A. Mitkas;Ioannis Vlahavas.
panhellenic conference on informatics (2005)
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