Predictive modeling using sparse logistic regression with applications
Manninen, Tapio (2014)
Tampere University of Technology
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In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classiﬁcation. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classiﬁcation method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a signiﬁcant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefﬁcient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classiﬁcation.
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