Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Feature Generation Using General Constructor Functions
Machine Learning
Journal of Artificial Intelligence Research
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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The present paper discusses the issue of enhancing classification performance by means other than improving the ability of certain Machine Learning algorithms to construct a precise classification model. On the contrary, we approach this significant problem from the scope of an extended coding of training data. More specifically, our method attempts to generate more features in order to reveal the hidden aspects of the domain, modeled by the available training examples. We propose a novel feature construction algorithm, based on the ability of Bayesian networks to represent the conditional independence assumptions of a set of features, thus projecting relational attributes which are not always obvious to a classifier when presented in their original format. The augmented set of features results in a significant increase in terms of classification performance, a fact that is depicted to a plethora of machine learning domains (i.e. data sets from the UCI ML repository and the Artificial Intelligence group) using a variety of classifiers, based on different theoretical backgrounds.