Title: On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers,
Abstract: Learning algorithms that learn linear models often have high representation
bias on real-world problems. In this paper, we show that this representation
bias can be greatly reduced by discretization. Discretization is a common
procedure in machine learning that is used to convert a quantitative attribute
into a qualitative one. It is often motivated by the limitation of some
learners to qualitative data. Discretization loses information, as fewer
distinctions between instances are possible using discretized data relative to
undiscretized data. In consequence, where discretization is not essential, it
might appear desirable to avoid it. However, it has been shown that
discretization often substantially reduces the error of the linear generative
Bayesian classifier naive Bayes. This motivates a systematic study of the
effectiveness of discretizing quantitative attributes for other linear
classifiers. In this work, we study the effect of discretization on the
performance of linear classifiers optimizing three distinct discriminative
objective functions --- logistic regression (optimizing negative
log-likelihood), support vector classifiers (optimizing hinge loss) and a
zero-hidden layer artificial neural network (optimizing mean-square-error). We
show that discretization can greatly increase the accuracy of these linear
discriminative learners by reducing their representation bias, especially on
big datasets. We substantiate our claims with an empirical study on $42$
benchmark datasets.