Athanasios Lagopoulos
Aristotle University of Thessaloniki
12 Papers
23 Citations
Athanasios Lagopoulos is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Computer science & Systematic review. The author has an hindex of 4, co-authored 11 publications.
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Papers
Learning-to-Rank and Relevance Feedback for Literature Appraisal in Empirical Medicine
Athanasios Lagopoulos,Antonios Anagnostou,Adamantios Minas,Grigorios Tsoumakas +3 more
- 10 Sep 2018
TL;DR: An incremental learning method that ranks documents, previously retrieved, by automating the process of title and abstract screening by combining a learning-to-rank model trained across multiple reviews with a model focused on the given review, incrementally trained based on relevance feedback.
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Web Robot Detection: A Semantic Approach
Athanasios Lagopoulos,Grigorios Tsoumakas,Georgios Papadopoulos +2 more
- 01 Nov 2018
TL;DR: This work presents a novel web robot detection approach for content-rich websites, based on the assumption that human web users are interested in specific topics, while web robots crawl the web randomly.
13
Using multi-target feature evaluation to discover factors that affect business process behavior
TL;DR: This work proposes a three-stage methodology designed to tackle challenges related to the general correlation problem of process mining, like dealing with general process behavior and relaxing the independence assumption among the elements of behavior.
12
Combining Inter-Review Learning-to-Rank and Intra-Review Incremental Training for Title and Abstract Screening in Systematic Reviews.
Antonios Anagnostou,Athanasios Lagopoulos,Grigorios Tsoumakas,Ioannis Vlahavas +3 more
- 01 Jan 2017
TL;DR: The approach combines a learning-to-rank model trained across multiple reviews with a model focused on the given review, incrementally trained based on relevance feedback, which shows promising results in title and abstract screening in diagnostic test accuracy reviews.
Content-aware web robot detection
TL;DR: A novel web robot detection approach that takes advantage of the content of a website based on the assumption that human web users are interested in specific topics, while web robots crawl the web randomly.
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