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Given a weighted, ordered query set\(Q\)and a partition of\(Q\)into classes, we study the problem of computing a minimum-cost decision tree that, given any query\(q\in Q\), uses equality tests and less-than tests to determine\(q\)'s class. Such a tree can be faster and smaller than a conventional search tree and smaller than a lookup table (both of which must identify\(q\), not just its class). We give the first polynomial-time algorithm for the problem. The algorithm extends naturally to the setting where each query has multiple allowed classes.
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Towards Recognizing Food Types for Unseen Subjects
Recognizing food types through sensor signals for unseen users remains remarkably challenging, despite extensive recent studies. The efficacy of prior machine learning techniques is dwarfed by giant variations of data collected from multiple participants, partly because users have varied chewing habits and wear sensor devices in various manners. This work treats the problem as an instance of the domain adaptation problem, where each user represents a domain. We develop the first multi-source domain adaptation (MSDA) method for food-typing recognition, which consists of three major components: stratified normalization, a multi-source domain adaptor, and adaptive ensemble learning. New techniques are developed for each component. Using a real-world dataset comprised of 15 participants, we demonstrate that our method achieves\(1.33\times\)to\(2.13\times\)improvement in accuracy compared with nine state-of-the-art MSDA baselines. Additionally, we perform an in-depth ablation study to examine the behavior of each component and confirm their efficacy.
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- PAR ID:
- 10559639
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Computing for Healthcare
- ISSN:
- 2637-8051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3696424
