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Oddness-based classification: A new way of exploiting neighbors

Abstract : The classification of a new item may be viewed as a matter of associating it with the class where it is the least at odds w.r.t. the elements already in the class. An oddness measure of an item with respect to a multiset, applicable to Boolean features as well as to numerical ones, has been recently proposed. It has been shown that cumulating this measure over pairs or triples (rather than larger subsets) of elements in a class could provide an accurate estimate of the global oddness of an item with respect to a class. This idea is confirmed and refined in the present paper. Rather than considering all the pairs in a class, one can only deal with the pairs whose an element is one of the nearest neighbors of the item, in the target class. The oddness evaluation computed on this basis still leads to good results in terms of accuracy. One can take a step further and choose the second element in the pair also as another nearest neighbor in the class. Although the method relies on the notion of neighbors, the resulting algorithm is far from being a variant of the classical k¿nearest neighbors approach. The oddness with respect to a class computed only on the basis of pairs made of two nearest neighbors leads to a low complexity algorithm. Experiments on a set of UCI benchmarks show that the classifier obtained can compete with other well¿known approaches.
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Submitted on : Wednesday, November 27, 2019 - 12:05:13 PM
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  • HAL Id : hal-02382723, version 1
  • OATAO : 24805


Myriam Bouhnas, Henri Prade, Gilles Richard. Oddness-based classification: A new way of exploiting neighbors. International Journal of Intelligent Systems, Wiley, 2018, 33, pp.2379-2401. ⟨hal-02382723⟩



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