Related Experiment Videos
Top-down induction of model trees with regression and splitting nodes.
Donato Malerba1, Floriana Esposito, Michelangelo Ceci
1Dipartimento di Informatica, Università degli Studi, via Orabona, 4, I-70125 Bari, Italy. malerba@di.uniba.it
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 6, 2004
Summary
The Stepwise Model Tree Induction (SMOTI) method efficiently builds accurate and interpretable model trees. SMOTI demonstrates superior performance over existing methods in various regression tasks.
Area of Science:
- Machine Learning
- Data Mining
- Statistical Modeling
Background:
- Model trees extend regression trees by associating leaves with multiple regression models.
- Data-driven construction of model trees is crucial for accurate predictive modeling.
Purpose of the Study:
- To introduce the Stepwise Model Tree Induction (SMOTI) method for constructing model trees.
- To evaluate the performance of SMOTI against existing model tree induction systems.
Main Methods:
- SMOTI employs a two-node system: regression nodes for straight-line regression and splitting nodes for feature space partitioning.
- Leaf models are constructed stepwise by combining regressions from the root to the leaf.
- Internal regression nodes have a global effect, while leaf regressions have local effects.
Main Results:
- SMOTI outperformed M5' and RETIS in accuracy on artificially generated datasets.
- On benchmark datasets, SMOTI showed better accuracy than RETIS.
- Statistical significance could not be determined when comparing SMOTI with M5' on benchmark datasets.
Conclusions:
- SMOTI is an effective method for data-driven model tree construction.
- SMOTI-induced model trees are generally simple, interpretable, and reveal interesting patterns.
- SMOTI offers a competitive alternative for regression and model tree induction.