Abstract
This work presents a multidimensional classifier described in terms of interaction factors called multidimensional k-interaction classifier. The classifier is based on a probabilistic model composed of the product of all the interaction factors of order lower or equal to k and it takes advantage of all the information contained in them. The proposed classifier does not require a model selection step and its complexity is controlled by the regularization parameter k. Multidimensional k-interaction classifier is a generalization of the Kikuchi-Bayes classifier (Jakulin et al. 2004) to the multidimensional classification problem. The proposed multidimensional classifier is especially appropriate for small k values and for low dimensional domains. Multidimensional k-interaction classifier has shown a competitive behavior in difficult artificial domains for which the low order marginal distributions are almost uniform.
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Pérez Martínes, A., Lozano, J.A., Inza, I. (2013). Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_40
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DOI: https://doi.org/10.1007/978-3-642-40643-0_40
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