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Abduction in Classification Tasks Maurizio Atzori, Paolo Mancarella, and Franco Turini
 

Summary: Abduction in Classification Tasks
Maurizio Atzori, Paolo Mancarella, and Franco Turini
Dipartimento di Informatica
University of Pisa, Italy
{atzori,paolo,turini}@di.unipi.it
Abstract. The aim of this paper is to show how abduction can be used
in classification tasks when we deal with incomplete data. Some classi-
fiers, even if based on decision tree induction like C4.5 [1], produce as
output a set of rules in order to classify new given examples. Most of these
rule-based classifiers make the assumption that at classification time we
can know all about new given examples. Probabilistic approaches make
rule-based classifiers able to get the most probable class, on the basis of
the frequency of the missing attribute in the training set [2]. This kind
of assumption sometimes leads to wrong classifications. We present an
abductive approach to (help to) choose which classification rule to ap-
ply when a new example with missing information needs to be classified,
using knowledge about the domain.
1 Introduction
Due to the availability of large amounts of data, easily collected and stored via
computer systems, the field of so-called data mining is gaining momentum. Sev-

  

Source: Atzori, Maurizio - Dipartimento di Informatica, UniversitÓ di Pisa

 

Collections: Computer Technologies and Information Sciences