Interactive Machine Learning and Visual Data Mining
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1654
Special Issue Editors
Interests: interactive machine learning; information retrieval; text classification; computational terminology; open science
Special Issues, Collections and Topics in MDPI journals
Interests: human-computer interaction; data visualization; knowledge management and representation; socio-technical design; ethics in AI
Special Issue Information
Dear Colleagues,
Successful machine learning applications require the correct use of the underlying training data in order to optimize and select the best models for a particular task. During this process, human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Interactive Machine Learning (IML) deals with this Special Issue by creating an efficient collaboration between users and machine learning algorithms.
In this field, Technology-assisted review (TAR) systems and eDiscovery use a kind of human-in-the-loop approach where classification and/or ranking algorithms are continuously trained according to the relevant feedback from expert reviewers until a substantial number of the relevant objects are identified.
Methods to combine (inter)-active machine learning and Visual Data Mining (VDM) interaction approaches through the iterative process with a human-in-the-loop strategy are part of the research questions addressed in this Special Issue.
Topics for this Special Issue are (but are not limited to):
- Design and implementation of interactive machine learning/data mining systems;
- Evaluation approaches/measures of IML and VDM systems;
- User studies and Evaluation protocols;
- Active Learning strategies for IML and VDM;
- Reproducibility of interactive systems.
Dr. Giorgio Maria Di Nunzio
Dr. Angela Locoro
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- interactive machine learning
- continuous active learning
- technology assisted review systems
- eDiscovery
- multilingual information retrieval
- computational terminology
- probabilistic models
