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.
|ジャーナル||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|出版ステータス||Published - 2004 12 9|
|イベント||17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada|
継続期間: 2004 5 17 → 2004 5 20
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)