When shortage of domain knowledge prevents us from chosing good attributes to represent the examples, learning is difficult. Expressing the target concept using only primitive (low-level) attributes may be complex, and the individual contribution of each attribute to the target's definition becomes insignificant. This aggravates attribute interaction (a situation in which complex relation-ships among attributes appear in the target concept). Then the learner needs to find relations and use them to help learning. This paper purports the relational operator projection as a useful tool to find relations. We describe MRP, a learning algorithm based on multidimensional relational projection, and compare it empirically to other learning systems. In spite of its simple search strategy, MRP performs well on synthetic concepts and real-world data. This advantage is attributed to the projection operator achieving the required functionality: finding relations.
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