An eager regression method based on best feature projections
Güvenir, H. A.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
217 - 226
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27626
This paper describes a machine learning method, called Regression by Selecting Best Feature Projections (RSBFP). In the training phase, RSBFP projects the training data on each feature dimension and aims to find the predictive power of each feature attribute by constructing simple linear regression lines, one per each continuous feature and number of categories per each categorical feature. Because, although the predictive power of a continuous feature is constant, it varies for each distinct value of categorical features. Then the simple linear regression lines are sorted according to their predictive power. In the querying phase of learning, the best linear regression line and thus the best feature projection are selected to make predictions. © Springer-Verlag Berlin Heidelberg 2001.
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Uysal, İ.; Güvenir, H. A. (Springer, 2004)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 ...
Guvenir, H. A.; Uysal, I. (Elsevier, 2000)This paper describes a machine learning method, called Regression on Feature Projections (RFP), for predicting a real-valued target feature, given the values of multiple predictive features. In RFP training is based on ...
Aydın, Tolga (Bilkent University, 2000)Two new machine learning methods, Regression by Selecting Best Feature Projections (RSBFP) and Regression by Selecting Best Features (RSBF), are presented for regression problems. These methods heavily make use of least ...