ISSN : 1796-203X
Volume : 1    Issue : 4    Date : July 2006

Local Boosting of Decision Stumps for Regression and Classification Problems
S. B. Kotsiantis, D. Kanellopoulos and P. E. Pintelas
Page(s): 30-37
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Numerous data mining problems involve an investigation of associations between features in
heterogeneous datasets, where different prediction models can be more suitable for different
regions. We propose a technique of boosting localized weak learners; rather than having constant
weights attached to each learner (as in standard boosting approaches), we allow weights to be
functions over the input domain. In order to find out these functions, we recognize local regions
having similar characteristics and then build local experts on each of these regions describing the
association between the data characteristics and the target value. We performed a comparison with
other well known combining methods on standard classification and regression benchmark
datasets using decision stump as based learner, and the proposed technique produced the most
accurate results.

Index Terms
classifier, machine learning, data mining, regressor