Intelligently Curating Machine Learning

Dear Colleagues,

Machine learning (ML), as a research area, has gained tremendous interest in recent years. This has led to significant improvement in theoretical model performance in many ML research areas, including computer vision and natural language processing. However, many of these improvements are demonstrated in research-oriented or synthesized datasets, and the question as to their effectiveness in collecting large volumes of high-quality data in practice to train, validate and test these models for real-world applications largely remains unanswered.

This Special Issue aims to promote original papers proposing novel methods to intelligently collect and curate high-quality data for machine learning models. It provides a unique forum for researchers and industry practitioners from a wide spectrum of domains to discuss challenges and opportunities in data collection and curation. In particular, the Guest Editors seek papers dealing with techniques, methodologies and best practices to improve the quality and efficiency of training and testing data collection and curation for machine learning models and applications.

We cordially invite you to submit a high-quality original research paper or review to this Special Issue of J—multidisciplinary scientific journal.

Dr. Yunqi Zhang
Dr. Yiping Kang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. J is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• data collection and curation
• crowdsourcing
• knowledge representation