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A lazy learning approach for building classification models
Author(s) -
Galván Inés M.,
Valls Joś M.,
García Miguel,
Isasi Pedro
Publication year - 2011
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20493
Subject(s) - computer science , artificial intelligence , machine learning , weighting , instance based learning , selection (genetic algorithm) , set (abstract data type) , function (biology) , active learning (machine learning) , medicine , evolutionary biology , biology , radiology , programming language
In this paper, we propose a lazy learning strategy for building classification learning models. Instead of learning the models with the whole training data set before observing the new instance, a selection of patterns is made depending on the new query received and a classification model is learnt with those selected patterns. The selection of patterns is not homogeneous, in the sense that the number of selected patterns depends on the position of the query instance in the input space. That selection is made using a weighting function to give more importance to the training patterns that are more similar to the query instance. Our intention is to provide a lazy learning mechanism suited to any machine learning classification algorithm. For this reason, we study two different methods to avoid fixing any parameter. Experimental results show that classification rates of traditional machine learning algorithms based on trees, rules, or functions can be improved when they are learnt with the lazy learning approach proposed. © 2011 Wiley Periodicals, Inc.