Work

Repairing non-monotone ordinal data sets by changing class \nlabels

Year: 2014

Type: report

Abstract: __Abstract__ \n \nOrdinal 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 \n \none 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.

Source: RePub (Erasmus University Rotterdam)

Authors: Pijls, Wim, Potharst, Rob

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7014932439

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