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Special issue description
Biomedical data presents several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although current research in this field has shown promising results, several research issues still need to be explored. Biomedical data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. The data often suffer from incompleteness, uncertainty and vagueness, which complicates conventional techniques of data mining ranging from the model, algorithm, system and application. An interesting aspect is to integrate different data sources in the biomedical data analysis process, which requires exploiting the existing domain knowledge from available sources. There is also a need to explore novel data mining methods in the biomedical research to improve predictive performance along with interpretation.
In the past, the evolution of research interest has focused on a relatively new area—granular computing (GrC), based on technologies such as fuzzy sets and rough sets. GrC provides a powerful tool for multiple granularity and multiple-view data analysis, which is of vital importance for understanding data driven analysis at different granularity levels. Biomedical data often contain a significant amount of unstructured, uncertain and imprecise data. GrC exhibits some strong capabilities and advantages in intelligent data analysis, pattern recognition, machine learning, and uncertain reasoning for biomedical data. GrC aims to find a suitable level of granularity of a given problem which can be adjusted according to the degree of fuzziness of the given problem. How to integrate GrC and data mining to combine their advantages is an interesting and important research topic. Granular Data Mining (GDM) is proposed to address this issue. Granular computing extracts knowledge from insufficient data, which can then be used in data mining for a new task/domain with big data.
Data mining based on granular computing in biomedical data analysis is an emerging field which crosses multiple research disciplines and industry domains. A vast number of real-world problems can be tackled using techniques encompassed in GrC. GDM research explores the advantages, and also challenges, derived from collecting and mining vast amounts of biomedical data.
The aims of this Special Issue in Information Sciences are: (1) to present the state-of-the-art research on granular data mining and its application in biomedical data, and (2) to provide a forum for researchers to discuss the latest progress, new research methodologies, and potential research topics.
The topics of this special issue include, but are not limited to:
Fuzzy set theory and application in biomedical data
Rough set theory and application in biomedical data
Fuzzy-rough data mining and rough-fuzzy data mining
Bio-inspired rough set and bio-inspired fuzzy rough set approaches
Fuzzy clustering technique for biomedical data
Novel/emerging forms of granular data mining
Granular computing framework for big data analytic
Granular data mining for feature learning, classification, regression, and clustering
Granular data mining for multi-task modeling, multi-view modeling and co-learning
Granular computing theory for biomedical applications
Granular fuzzy set algorithm and application in biomedical data
Granular rough set algorithm and application in biomedical data
Fuzzy knowledge retrieval of medical images
Granular data mining for large-scale image and multimedia processing
Granular data mining for brain-machine interfaces and medical signal analysis
Biomedical image mining, and video analysis
Application of fuzzy data processing technology in large-scale healthcare data
We highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Submission format
Papers will be evaluated based on their originality, presentation, relevance and contribution to current trends of granular data mining approach for biomedical data analysis as well as their suitability and the quality in terms of both technical contribution and writing. The submitted papers must be written in English and describe original research which has not been published nor currently under review by other journals or conferences. Previously published conference papers should be clearly identified by the authors (at the submission stage) and an explanation should be provided about how the papers have been extended to be considered for this special issue.
Guest Editors will make an initial judgment of the suitability of submissions to this special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly informed in such cases.
Author guidelines for preparation of manuscript can be found at www.elsevier.com/locate/ins
Submission guidelines
All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “VSI: Granular Data Mining Approach-BDA” when they identify the “Article Type” step in the submission process. The EES website is located at http://ees.elsevier.com/ins/
Guide for authors
This site will guide you stepwise through the creation and uploading of your article. The guide for authors can be found on the journal homepage (www.elsevier.com/ins).
Important dates
Deadline of submission: December 31, 2018
Revised version submission: February 28, 2019
Acceptance notification: March 31, 2019
Final manuscripts due: April 30, 2019
Anticipated publication: June 30, 2019
Guest editors
Weiping Ding, Nantong University, China
[email protected]
Chin-Teng Lin, University of Technology Sydney, Australia
[email protected]
Alan Wee-Chung Liew, Griffith University, Australia
[email protected]
Isaac Triguero, University of Nottingham, United Kingdom
[email protected]
Wenjian Luo, University of Science and Technology of China
[email protected]
For inquiries regarding this Special Issue, please contact: Weiping Ding ([email protected] )
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