Report cards for manholes: Eliciting expert feedback for a learning task
Name
Radeva-2009-Report cards for manholes Eliciting expert feedback for a learning task.pdf
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640.59 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Radeva, Axinia
Rudin, Cynthia
Passonneau, Rebecca
Isaac, Delfina F.
Date Issued
January 2010
Journal
International Conference on Machine Learning and Applications, 2009. ICMLA '09.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Radeva, A. et al. “Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task.” Machine Learning and Applications, 2009. ICMLA '09. International Conference on. 2009. 719-724. © Copyright 2009 IEEE
Version
Final published version
Abstract
We present a manhole profiling tool, developed as part of the Columbia/Con Edison machine learning project on manhole event prediction, and discuss its role in evaluating our machine learning model in three important ways: elimination of outliers, elimination of falsely predictive features, and assessment of the quality of the model. The model produces a ranked list of tens of thousands of manholes in Manhattan, where the ranking criterion is vulnerability to serious events such as fires, explosions and smoking manholes. Con Edison set two goals for the model, namely accuracy and intuitiveness, and this tool made it possible for us to address both of these goals. The tool automatically assembles a "report card" or "profile" highlighting data associated with a given manhole. Prior to the processing work that underlies the profiling tool, case studies of a single manhole took several days and resulted in an incomplete study; locating manholes such as those we present in this work would have been extremely difficult. The model is currently assisting Con Edison in determining repair priorities for the secondary electrical grid.
MIT Department
Sloan School of Management
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ICMLA.2009.72
