Repairing non-monotone ordinal data sets by changing class labels

Wim Pijls and Rob Potharst

No EI 2014-29, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: __Abstract__ Ordinal data sets often contain a certain amount of non-monotone noise. This paper proposes three algorithms for removing these non-monotonicities by relabeling the noisy instances. The first one is a naive algorithm. The second one is a refinement of this naive algorithm which minimizes the difference between the old and the new label. The third one is optimal in the sense that the number of unchanged instances is maximized. The last algorithm is a refinement of the second. In addition, the runtime complexities are discussed.

Keywords: Ordinal; data; sets (search for similar items in EconPapers)
Pages: 9
Date: 2014-11-01
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:77641

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