Browsing by Subject "Feature Projection"

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    An eager regression method based on best feature projections
    (Springer, Berlin, Heidelberg, 2001) Aydın, Tolga; Güvenir, H. Altay
    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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    Maximizing benefit of classifications using feature intervals
    (Springer, Berlin, Heidelberg, 2003) İkizler, Nazlı; Güvenir, H. Altay
    There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained.