Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2017

Abstract

The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint PART-WHOLE outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset. © 2017 ACM.

Keywords

Joint predictive model, part-whole relationship

Discipline

Databases and Information Systems | Data Storage Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, 2020 August 13-17

First Page

295

Last Page

304

Identifier

10.1145/3097983.3098006

Publisher

ACM

City or Country

Halifax, Nova Scotia, Canada

Additional URL

https://doi.org/10.1145/3097983.3098006

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