The aim of this paper is to show how abduction can be used in classification tasks when we deal with incomplete data. Some classifiers, 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 apply when a new example with missing information needs to be classified, using knowledge about the domain.
Abduction in Classification Tasks
MANCARELLA, PAOLO MARIA;TURINI, FRANCO
2003-01-01
Abstract
The aim of this paper is to show how abduction can be used in classification tasks when we deal with incomplete data. Some classifiers, 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 apply when a new example with missing information needs to be classified, using knowledge about the domain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
