Regression by Feature Projections

  • İlhan Uysal
  • H. Altay Güvenir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1704)


This paper describes a machine learning method, called Regression by Feature Projections (RFP), for predicting a real-valued target feature. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction is computed through two approximation procedures. The first approximation process is to find the individual predictions of features by using the K-nearest neighbor algorithm (KNN). The second approximation process combines the predictions of all features. During the first approximation step, each feature is associated with a weight in order to determine the prediction ability of the feature at the local query point. The weights, found for each local query point, are used in the second step and enforce the method to have an adaptive or context-sensitive nature. We have compared RFP with the KNN algorithm. Results on real data sets show that RFP is much faster than KNN, yet its prediction accuracy is comparable with the KNN algorithm.


Mean Square Error Query Point Irrelevant Feature Local Weight Feature Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • İlhan Uysal
    • 1
  • H. Altay Güvenir
    • 1
  1. 1.Department of Computer Engineering and Information SciencesBilkent UniversityAnkaraTurkey

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