Challenges and Opportunities in Applied Machine Learning

Machine‐learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (for example, accuracy or AUC) to that of existing classificati...

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Bibliographic Details
Published in:The AI magazine 2012-03, Vol.33 (1), p.11-24
Main Authors: Brodley, Carla E., Rebbapragada, Umaa, Small, Kevin, Wallace, Byron C.
Format: Article
Language:English
Subjects:
Active learning
Algorithm
Algorithms
Artificial intelligence
Automatic classification
Citations
Classification
Collaboration
Data
Data mining
Data warehousing/data mining
Datasets
Evidence-based medicine
Evidence-based practice
Information retrieval
Literature reviews
Machine learning
Medical research
Medicine
Subject specialists
Systematic review
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Summary:Machine‐learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (for example, accuracy or AUC) to that of existing classification models on publicly available data sets. In terms of advancing machine learning as an academic discipline, this approach has thus far proven quite fruitful. However, it is our view that the most interesting open problems in machine learning are those that arise during its application to real‐world problems. We illustrate this point by reviewing two of our interdisciplinary collaborations, both of which have posed unique machine‐learning problems, providing fertile ground for novel research.
ISSN:0738-4602
2371-9621
DOI:10.1609/aimag.v33i1.2367