Not being at odds with a class: A new way of exploiting neighbors for classification

Publication Type:
Conference Proceeding
Citation:
Frontiers in Artificial Intelligence and Applications, 2016, 285 pp. 1662 - 1663
Issue Date:
2016-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
FAIA285-1662.pdfPublished version194.28 kB
Adobe PDF
© 2016 The Authors and IOS Press. 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.
Please use this identifier to cite or link to this item: