Instance-based regression by partitioning feature projections

doi:10.1023/B:APIN.0000027767.87895.b2

Instance-based regression by partitioning feature projections

2004-07-01

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DOI：10.1023/B:APIN.0000027767.87895.b2

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A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projection-based approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data.

Keywords:

machine learning

regression

feature projections

Instance-based regression by partitioning feature projections

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