Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations

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Author(s): Xenia Naidenova (Military Medical Academy, Russia)
Copyright: 2008
Pages: 24
Source title: Mathematical Methods for Knowledge Discovery and Data Mining
Source Author(s)/Editor(s): Giovanni Felici (Consiglio Nazionale delle Richerche, Italy)and Carlo Vercellis (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-59904-528-3.ch003

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

The purpose of this paper is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using well-known deduction and induction logical rules. The concept of a good classification (diagnostic) test for a given set of positive examples lies in the basis of our approach to the machine learning problems. The task of inferring all good diagnostic tests is formulated as searching the best approximations of a given classification (a partitioning) on a given set of examples. The lattice theory is used as a mathematical language for constructing good classification tests. The algorithms of good tests inference are decomposed into subtasks and operations that are in accordance with main human commonsense reasoning rules.

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Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations

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