Building and solving odd-one-out classification problems: A systematic approach

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.

► A systematic method for building classification problems is introduced.
► A new psychometric test of inductive reasoning, the R-ASCM, is presented.
► The average Hamming distance solves 97% of the classification problems.
► The new algorithm improves categorical case-based reasoning and k-NN algorithms.
► The new algorithm helps understand the cognitive basis of classification.