التفاصيل البيبلوغرافية
| العنوان: |
Semi-supervised weighting for averaged one-dependence estimators. |
| المؤلفون: |
Wang, Limin, Zhang, Shuai, Mammadov, Musa, Li, Kuo, Zhang, Xinhao, Wu, Siyuan |
| المصدر: |
Applied Intelligence; Mar2022, Vol. 52 Issue 4, p4057-4073, 17p |
| مصطلحات موضوعية: |
SUPERVISED learning, WEIGHT training, MACHINE learning, MACHINE tools |
| مصطلحات جغرافية: |
IRVINE (Calif.) |
| الشركة/الكيان: |
UNIVERSITY of California (System) |
| مستخلص: |
Averaged one-dependence estimators (AODE) is a state-of-the-art machine learning tool for classification due to its simplicity, high computational efficiency, and excellent classification accuracy. Weighting provides an effective mechanism to ensemble superparent one-dependence estimators (SPODEs) in AODE by linearly aggregating their weighted probability estimates. Supervised weighting and unsupervised weighting are proposed to learn weights from labeled or unlabeled data, whereas their interoperability has not previously been investigated. In this paper, we propose a novel weighting paradigm in the framework of semi-supervised learning, called semi-supervised weighting (SSW). Two different versions of weighted AODEs, supervised weighted AODE (SWAODE) which performs weighting at training time and unsupervised weighted AODE (UWAODE) which performs weighting at classification time, are built severally. Log likelihood function is introduced to linearly aggregate the outcomes of these two weighted AODEs. The proposed algorithm, called SSWAODE, is validated on 38 benchmark datasets from the University of California at Irvine (UCI) machine learning repository and the experimental results prove the effectiveness and robustness of SSW for weighting AODE in terms of zero-one loss, bias, variance and etc. SSWAODE well achieves the balance between the ground-truth dependencies approximation and the effectiveness of probability estimation. [ABSTRACT FROM AUTHOR] |
|
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder