Machine Learning for Science and Society

A Special Issue of Machine Learning

Cynthia Rudin and Kiri L. Wagstaff, guest editors

Accepted papers:

Call For Papers: Submissions Due: November 16, 2012

In this special issue, we will showcase papers that address problems of importance to science and society. Machine learning and data mining have been used, and will continue to be used, in many important domains that affect people's lives every day; however, it is becoming less common in many mainstream machine learning venues to publish work whose primary goal is to have impact on a new real-world problem. The collection of papers in this special issue will provide an updated answer to "what is machine learning good for?" in which impact is the guiding principle.

For many domains in which machine learning presently makes an impact, it is not necessarily the case that the precise choice of machine learning algorithm is the key factor for success in the domain. Choices relating to problem formulation and data representation sometimes matter far more. Further, there can be several criteria for success beyond predictive performance, including the cost of different errors, domain-specific operational constraints, the interpretability of the system's output, and factors limiting or enabling domain experts to make use of the results. We seek papers that address these issues and present lessons that can benefit the community as a whole.

If you are considering submitting, you may find it useful to look at the guidelines for reviewers in advance.

Papers submitted to this issue may center around:

When preparing manuscripts, authors might find it helpful to consider the full process of knowledge discovery (KDD or CRISP-DM) including business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

It is strongly encouraged, but not required, to have a relevant domain expert as a co-author.

Example challenges that affect science and/or society:

Papers submitted to this special edition must be scientific, in that they must contain a message that is potentially useful to future practitioners, as opposed to simply reporting an anecdotal experience. Papers that only describe a domain by which they are motivated, then present an empirical comparison ("bake-off") or a new algorithm as the main result, are likely to be rejected without review. Submissions about domains for which there is already a well-established and long-standing mechanism for success through machine learning are less likely to be accepted.

Submission Guidelines

The papers for this special edition should be short papers, approximately 8-12 pages in length. Authors should submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. We aim for a fast turnaround time for reviews to get decisions out quickly (see the timeline below).

Submissions to the special issue must be submitted like regular submissions to the journal. Instructions can be found here. You can download LaTeX style files or a Word template.

We prefer papers that are structured as follows:

If you are considering submitting to the special issue and have questions regarding the scope or need further information, please do not hesitate to contact the editors:

Cynthia Rudin and Kiri L. Wagstaff,

Administrative notes:


Submit title+abstract (by email): November 12, 2012
Submission deadline:November 16, 2012
Early submissions are welcomed and will receive an earlier review and response.
Decisions (for on-time submisions, estimated):December 21, 2012 January 4, 2013
Revisions due:January 11, 2013 January 25, 2013
Decisions (estimated):February 10, 2013
Final version due:March 1, 2013
Special issue published:Summer or fall of 2013