Search

Object selection is an important task for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented. [ABSTRACT FROM AUTHOR]

In supervised classification, the object selection or instance selection is an important task, mainly for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers. [ABSTRACT FROM AUTHOR]

In this work, a fast k most similar neighbor (k-MSN) classifier for mixed data is presented. The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition. Many fast k-NN classifiers have been developed to be applied on numerical object descriptions, most of them based on metric properties to avoid object comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical features (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, our classifier is based on search algorithms suitable for mixed data and non-metric comparison functions. Some experiments and a comparison against other two fast k-NN methods, using standard databases, are presented. [ABSTRACT FROM AUTHOR]

The nearest neighbor (NN) classifier has been a widely used technique in pattern recognition because of its simplicity and good behavior. To decide the class of a new object, the NN classifier performs an exhaustive comparison between the object to classify and the training set T. However, when T is large, the exhaustive comparison is very expensive and sometimes becomes inapplicable. To avoid this problem, many fast NN algorithms have been developed for numerical object descriptions, most of them based on metric properties to avoid comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical attributes (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, in this paper a fast most similar object classifier based on search methods suitable for mixed data is presented. Some experiments using standard databases and a comparison with other two fast NN methods are presented. [ABSTRACT FROM AUTHOR]