MeFirst ranking and multiple dichotomies : Via Linear Programming and Neural Networks

Individuals, institutions and even cities and countries are often ranked according to some linear weighting of their attributes. Under commonly prevailing conditions, it is possible to find weights that give top rank to most arbitrarily designated entries. The number of entries may exceed the number...

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Bibliographic Details
Published inInternational Conference on Pattern Recognition pp. 550 - 556
Main Authors Nagy, George, Krishnamoorthy, Mukkai
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.08.2022
Subjects
dimensional augmentation
halfspace dichotomy
Linear programming
Neural networks
normalization
Pattern recognition
Probabilistic logic
Sufficient conditions
Training
unbalanced classes
Urban areas
Online AccessGet full text
ISSN2831-7475
DOI10.1109/ICPR56361.2022.9956323

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Summary:Individuals, institutions and even cities and countries are often ranked according to some linear weighting of their attributes. Under commonly prevailing conditions, it is possible to find weights that give top rank to most arbitrarily designated entries. The number of entries may exceed the number of attributes by orders of magnitude. Necessary and sufficient conditions on the subject-attribute matrix are derived in terms of one-against-all halfplane dichotomies and convex hulls. Pairwise attribute difference vectors are more effective than attribute vectors for one-against-all classification. Comparisons of MeFirst algorithms based on neural networks and on linear programming (LP), on datasets drawn from published rankings of scientists and universities, show that on these tasks, LP is significantly faster.
ISSN:2831-7475
DOI:10.1109/ICPR56361.2022.9956323