Building and solving odd-one-out classification problems: A systematic approach |
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Authors: | Philippe E. Ruiz |
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Affiliation: | Université Lille Nord-de-France, LSMRC, SKEMA, Avenue Willy Brandt, 59777 Euralille, France Université Lille Nord-de-France, Lab. URECA (EA 1059), Domaine du Pont de Bois, 59653 Villeneuve d'Ascq, France |
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Abstract: | Classification problems (“find the odd-one-out”) are frequently used as tests of inductive reasoning to evaluate human or animal intelligence. This paper introduces a systematic method for building the set of all possible classification problems, followed by a simple algorithm for solving the problems of the R-ASCM, a psychometric test derived from this method. The average Hamming distance finds repetitions of features between and within the problems' sets; it manages to solve 97% of such problems. This performance is equaled only by superior human adults. Finally, these results demonstrate that a simple two-step algorithm can improve categorical case-based reasoning and k-NN algorithms while clarifying the cognitive basis of classification. |
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Keywords: | Fluid intelligence Inductive reasoning Categorical classification Odd-one-out clustering Hamming distance R-ASCM |
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