@inproceedings{Modi2005,
title = {Classification of examples by multiple agents with private features},
author = {Pragnesh Jay Modi and Peter Woo Tae Kim},
booktitle = {IEEE/WIC/ACM International Conference on Intelligent Agent Technology},
month = {September},
pages = {223--229},
url = {http://www.cs.cmu.edu/afs/cs/user/mjs/ftp/thesis-05/kim.pdf},
year = {2005},
abstract = {We consider classification tasks where relevant features are distributed
among a set of agents and cannot be centralized, for example due
to privacy restrictions. We are motivated by a key classification
task that arises in a calendar management domain where software assistants
classify new meetings as likely to be difficult to schedule. Accurate
prediction of the output class is difficult for an isolated single
agent because the target concept may involve features to which the
agent does not have access, for example each attendee's willingness
to attend the meeting. To increase prediction accuracy, novel learning
algorithms are required in which agents collaborate to classify new
examples while maintaining the privacy of features. We introduce
a novel distributed asynchronous decision-tree inspired algorithm
for such tasks named DDT. DDT differs from previous approaches in
that it applies to vertically partitioned data with categorical multi-valued
features, it requires no explicit hypothesis generation, and there
is no a priori restriction on number of agents. We present empirical
results in our meeting scheduling domain and show that DDT outperforms
a single agent learner and performs as well as a centralized learner
with hypothetical access to all the features.},
keywords = {Agents Classification DDM }
}
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