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This paper presents our research in multi-class pattern learning from imbalanced data. In many real world applications, the data among different pattern classes are imbalanced; some classes may have far more training data than the others. Typically a neural network classifier has troubles to learn from the imbalanced data distribution among different pattern classes. In this paper we propose a new pattern classification algorithm, One-Against-Higher-Order (OAHO), that effectively learn multi-class patterns from the imbalanced data, and a theoretical analysis of data imbalance problem related to other popular multi-class pattern classification approaches. We have conducted experiments on the two highly imbalanced data sets posted at the UCI site, and the results show that the neural network system trained with the proposed OAHO algorithm gives better performances on minority pattern classes over the neural network systems trained with the two other popular multi-class classification methods: OAO and OAA.