The article introduces a new method for reducing the number of attributes in complex data sets. By using a combination of discernibility matrix and sequential approach, the algorithm creates a series of discernibility functions to identify important attributes. This method efficiently builds an attribute reduction tree by focusing on core attributes, resulting in significant reductions. The algorithm is shown to be faster than other existing methods, making it a promising tool for handling high-dimensional data.