Abstract
We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.
| Original language | English |
|---|---|
| Pages (from-to) | 119-134 |
| Number of pages | 16 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 129 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
Cite this
}
Greedy active learning algorithm for logistic regression models. / Hsu, Hsiang Ling; Chang, Yuan chin Ivan; Chen, Ray-Bing.
In: Computational Statistics and Data Analysis, Vol. 129, 01.01.2019, p. 119-134.Research output: Contribution to journal › Article
TY - JOUR
T1 - Greedy active learning algorithm for logistic regression models
AU - Hsu, Hsiang Ling
AU - Chang, Yuan chin Ivan
AU - Chen, Ray-Bing
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.
AB - We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.
UR - http://www.scopus.com/inward/record.url?scp=85053065230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053065230&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2018.08.013
DO - 10.1016/j.csda.2018.08.013
M3 - Article
VL - 129
SP - 119
EP - 134
JO - Computational Statistics and Data Analysis
T2 - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
ER -