Clustered partial linear regression

• Type: Article in International Scientific Journal
• Date: 2000

Authors

Torgo, L (Author) FEP; da Costa, JP (Author) FCUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: MACHINE LEARNING: ECML 2000

Vol. 8

Pages: 426-436

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-001-1VT

DOI: 10.1007/3-540-45164-1_43

Abstract (EN): This paper presents a new method that deals with a supervised learning task usually known as multivariate regression. The main distinguishing feature of this new technique is the use of a clustering method to obtain sub-sets of the training data before the learning phase. After this "resampling" process a different regression model is fitted to each found cluster. We call the resulting method clustered partial linear regression. Predictions using this technique are preceded by a cluster membership query for each test case. The cluster membership probability of a test case is used as a weight in an averaging process that calculates the final prediction. This averaging process involves the predictions of the regression models associated to the clusters for which the test case may belong. We have tested this general multi-strategy approach using several regression techniques and we have observed significant accuracy gains in several data sets. We have also compared our method to bagging that also uses an averaging process to obtain predictions. This experiment showed that the two methods are significantly different. Finally, we present a comparison of our method with several state-of-the-art regression methods.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11