Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
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Shah_MInd the gap.pdf
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Author(s)
Doshi-Valez, Finale
Kim, Been
Shah, Julie A
Date Issued
2015
Journal
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation Inc.
Citation
Kim, Been, Julie A Shah, and Finale Doshi-Velez. “Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction.” Advances in Neural Information Processing Systems 28. Ed. C Cortes et al. Curran Associates, Inc., 2015. 2260–2268.
Version
Final published version
Abstract
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM's ability to help with dataset exploration
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5957-mind-the-gap-a-generative-approach-to-interpretable-feature-selection-and-extraction
