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Oddness-Based Classifiers for Boolean or Numerical Data.

, , and . KI, volume 9324 of Lecture Notes in Computer Science, page 32-44. Springer, (2015)

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Oddness-based classification: A new way of exploiting neighbors., , and . Int. J. Intell. Syst., 33 (12): 2379-2401 (2018)Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features., , , and . CIARP (1), volume 8258 of Lecture Notes in Computer Science, page 165-173. Springer, (2013)On the Induction of Different Kinds of First-Order Fuzzy Rules., , and . ECSQARU, volume 2711 of Lecture Notes in Computer Science, page 370-381. Springer, (2003)A New View of Conformity and Its Application to Classification., , and . ECSQARU, volume 9161 of Lecture Notes in Computer Science, page 221-232. Springer, (2015)Analogy, Paralogy and Reverse Analogy: Postulates and Inferences., and . KI, volume 5803 of Lecture Notes in Computer Science, page 306-314. Springer, (2009)Oddness-Based Classifiers for Boolean or Numerical Data., , and . KI, volume 9324 of Lecture Notes in Computer Science, page 32-44. Springer, (2015)Trying to Understand How Analogical Classifiers Work., , and . SUM, volume 7520 of Lecture Notes in Computer Science, page 582-589. Springer, (2012)Solving Word Analogies: A Machine Learning Perspective., , and . ECSQARU, volume 11726 of Lecture Notes in Computer Science, page 238-250. Springer, (2019)From Analogical Proportion to Logical Proportions., and . Logica Universalis, 7 (4): 441-505 (2013)Constructive Solving of Raven's IQ Tests with Analogical Proportions., , and . Int. J. Intell. Syst., 31 (11): 1072-1103 (2016)