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 of the target value for a query point is obtained through two approximationprocedures executed sequentially.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 predication ability of the feature at the local query point.The weights,found for each local query point,are used in the second step adn 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 predication accuracy is comparable with the KNN algorithm.
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