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This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, M-of-N, and X-of-N representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With different option settings, it can construct four different types of new attribute, but all other factors are fixed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts. Moreover, the study demonstrates that the stronger representation power of M-of-N than conjunction and disjunction and the stronger representation power of X-of-N than these three types of new attribute can be reflected in the performance of decision tree learning.