For the problem that the traditional classification methods often tend to the majority class and lead a lower
classification accuracy to the minority class in imbalanced data, a new calculation method of fuzzy rule weights is proposed.
This algorithm not only keeps the pattern matching degree within class in uniform distribution, but also enhances the contrast
of inter-class. Then a classification algorithm is designed, which includes the new calculation method of fuzzy rule weights,
Chi et al algorithm and fuzzy reasoning model. Finally numerical simulation about the imbalanced data of UCI data sets
shows the reliability of the classification algorithm.