Abstract
Here is discussed what is constructive meta-learning and how it goes well compared with selective meta-learning that already becomes popular. Selective meta-learning takes multiple learning schemes with the following different ways: bagging, boosting, cascading and stacking methods. On the other hand, constructive meta-learning constructs the learning scheme proper to a given data set. We have implemented constructive meta-learning by recomposing methods into learning schemes with mining (inductive learning) method repositories that come from decomposition of popular mining algorithms. To evaluate our constructive meta-learning, we have done the comparison of the performances of our constructive meta-learning and those of two stacking methods, using UCI/ML common data sets. It has shown us that our constructive meta-learning goes better than the two stacking methods. Furthermore, it turns out to be promising that we apply constructive meta-learning to meta-learner in selective meta-learning.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Editors | B. Orchard, C. Yang, M. Ali |
| Pages | 502-511 |
| Number of pages | 10 |
| Volume | 3029 |
| Publication status | Published - 2004 |
| Externally published | Yes |
| Event | 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada Duration: 2004 May 17 → 2004 May 20 |
Other
| Other | 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 |
|---|---|
| Country | Canada |
| City | Ottowa, Ont. |
| Period | 04/5/17 → 04/5/20 |
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ASJC Scopus subject areas
- Hardware and Architecture
Cite this
Constructive meta-learning with machine learning method repositories. / Abe, Hidenao; Yamaguchi, Takahira.
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / B. Orchard; C. Yang; M. Ali. Vol. 3029 2004. p. 502-511.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Constructive meta-learning with machine learning method repositories
AU - Abe, Hidenao
AU - Yamaguchi, Takahira
PY - 2004
Y1 - 2004
N2 - Here is discussed what is constructive meta-learning and how it goes well compared with selective meta-learning that already becomes popular. Selective meta-learning takes multiple learning schemes with the following different ways: bagging, boosting, cascading and stacking methods. On the other hand, constructive meta-learning constructs the learning scheme proper to a given data set. We have implemented constructive meta-learning by recomposing methods into learning schemes with mining (inductive learning) method repositories that come from decomposition of popular mining algorithms. To evaluate our constructive meta-learning, we have done the comparison of the performances of our constructive meta-learning and those of two stacking methods, using UCI/ML common data sets. It has shown us that our constructive meta-learning goes better than the two stacking methods. Furthermore, it turns out to be promising that we apply constructive meta-learning to meta-learner in selective meta-learning.
AB - Here is discussed what is constructive meta-learning and how it goes well compared with selective meta-learning that already becomes popular. Selective meta-learning takes multiple learning schemes with the following different ways: bagging, boosting, cascading and stacking methods. On the other hand, constructive meta-learning constructs the learning scheme proper to a given data set. We have implemented constructive meta-learning by recomposing methods into learning schemes with mining (inductive learning) method repositories that come from decomposition of popular mining algorithms. To evaluate our constructive meta-learning, we have done the comparison of the performances of our constructive meta-learning and those of two stacking methods, using UCI/ML common data sets. It has shown us that our constructive meta-learning goes better than the two stacking methods. Furthermore, it turns out to be promising that we apply constructive meta-learning to meta-learner in selective meta-learning.
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M3 - Conference contribution
VL - 3029
SP - 502
EP - 511
BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
A2 - Orchard, B.
A2 - Yang, C.
A2 - Ali, M.
ER -