Not Being at Odds with a Class: A New Way of Exploiting Neighbors for Classification

Publisher:
IOS Press
Publication Type:
Conference Proceeding
Citation:
Proceedings of the Twenty-second European Conference on Artificial Intelligence, 2016, 285 pp. 1662 - 1663
Issue Date:
2016
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Classification can be viewed as a matter of associating a new item with the class where it is the least at odds w.r.t. the other elements. A recently proposed oddness index applied to pairs or triples (rather than larger subsets of elements in a class), when summed up over all such subsets, provides an accurate estimate of a global oddness of an item w.r.t. a class. Rather than considering all pairs in a class, one can only deal with pairs containing one of the nearest neighbors of the item in the target class. Taking a step further, we choose the second element in the pair as another nearest neighbor in the class. The oddness w.r.t. a class computed on the basis of pairs made of two nearest neighbors leads to low complexity classifiers, still competitive in terms of accuracy w.r.t. classical approaches.
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