Abstract
In this paper we consider dynamical properties of simple iterative relational classifiers. We conjecture that for a class of algorithms that use label-propagation the iterative procedure can lead to non-trivial dynamics in the number of newly classified instances. The underlaying reason for this non-triviality is that in relational networks true class labels are likely to propagate faster than false ones. We suggest that this phenomenon, which we call two-tiered dynamics for binary classifiers, can be used for establishing a self-consistent classification threshold and a criterion for stopping iteration. We demonstrate this effect for two unrelated binary classification problems using a variation of a iterative relational neighbor classifier. We also study analytically the dynamical properties of the suggested classifier, and compare its results to the numerical experiments on synthetic data.
| Original language | English (US) |
|---|---|
| Title of host publication | IJCAI International Joint Conference on Artificial Intelligence |
| Pages | 708-713 |
| Number of pages | 6 |
| State | Published - 2005 |
| Externally published | Yes |
| Event | 19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom Duration: Jul 30 2005 → Aug 5 2005 |
Other
| Other | 19th International Joint Conference on Artificial Intelligence, IJCAI 2005 |
|---|---|
| Country | United Kingdom |
| City | Edinburgh |
| Period | 7/30/05 → 8/5/05 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Inferring useful heuristics from the dynamics of iterative relational classifiers. / Galstyan, Aram; Cohen, Paul R.
IJCAI International Joint Conference on Artificial Intelligence. 2005. p. 708-713.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Inferring useful heuristics from the dynamics of iterative relational classifiers
AU - Galstyan, Aram
AU - Cohen, Paul R
PY - 2005
Y1 - 2005
N2 - In this paper we consider dynamical properties of simple iterative relational classifiers. We conjecture that for a class of algorithms that use label-propagation the iterative procedure can lead to non-trivial dynamics in the number of newly classified instances. The underlaying reason for this non-triviality is that in relational networks true class labels are likely to propagate faster than false ones. We suggest that this phenomenon, which we call two-tiered dynamics for binary classifiers, can be used for establishing a self-consistent classification threshold and a criterion for stopping iteration. We demonstrate this effect for two unrelated binary classification problems using a variation of a iterative relational neighbor classifier. We also study analytically the dynamical properties of the suggested classifier, and compare its results to the numerical experiments on synthetic data.
AB - In this paper we consider dynamical properties of simple iterative relational classifiers. We conjecture that for a class of algorithms that use label-propagation the iterative procedure can lead to non-trivial dynamics in the number of newly classified instances. The underlaying reason for this non-triviality is that in relational networks true class labels are likely to propagate faster than false ones. We suggest that this phenomenon, which we call two-tiered dynamics for binary classifiers, can be used for establishing a self-consistent classification threshold and a criterion for stopping iteration. We demonstrate this effect for two unrelated binary classification problems using a variation of a iterative relational neighbor classifier. We also study analytically the dynamical properties of the suggested classifier, and compare its results to the numerical experiments on synthetic data.
UR - http://www.scopus.com/inward/record.url?scp=84880759180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880759180&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84880759180
SP - 708
EP - 713
BT - IJCAI International Joint Conference on Artificial Intelligence
ER -