Name
Title | ||
|---|---|---|
Target Learning: A Novel Framework to Mine Significant Dependencies for Unlabeled Data. |
Abstract | ||
|---|---|---|
To mine significant dependencies among predictive attributes, much work has been carried out to learn Bayesian netwrok classifiers (BNC(T)s) from labeled training data set T. However, if BNCT does not capture the "right" dependencies that would be most relevant to unlabeled testing instance, that will result in performance degradation. To address this issue we propose a novel framework, called target learning, that takes each unlabeled testing instance as a target and builds an "unstable" Bayesian model BNCP for it. To make BNCP and BNCT complementary to each other and work efficiently in combination, the same learning strategy is applied to build them. Experimental comparison on 32 large data sets from UCI machine learning repository shows that, for BNCs with different degrees of dependence target learning always helps improve the generalization performance with minimal additional computation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-93034-3_9 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT I |
Keywords | Field | DocType |
Bayesian network,Target learning,Unlabeled data | Training set,Data set,Bayesian inference,Computer science,Bayesian network,Artificial intelligence,Machine learning,Bayesian probability,Computation | Conference |
Volume | ISSN | Citations |
10937 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| LiMin Wang | 1 | 816 | 48.41 |
| Shenglei Chen | 2 | 18 | 4.05 |
| Musa A. Mammadov | 3 | 51 | 9.48 |