<p>The Granular-ball Rough Set (GBRS) model is a promising approach that combines classical rough sets with neighborhood rough sets. However, GBRS suffers from the issue of high randomness in its granular-ball generation process, leading to instability and inconsistent partitioning results. Additionally, it does not take into account the correlation between conditional attributes and the decision attribute, which can affect the accuracy and effectiveness of attribute reduction. To address these issues, we propose the Integrated Weighted Granular-ball Rough Set (IWGBRS) model, which integrates multiple granular-ball splitting strategies to achieve more stable and reliable partitions. The IWGBRS model introduces a novel granular-ball generation algorithm that employs a combination of various clustering methods for initial data partitioning. The IWGBRS model reduces the randomness in the granular-ball generation process and ensures more consistent and stable partitioning results. Furthermore, to address the issue of not considering the correlation between conditional attributes and the decision attribute in GBRS, the IWGBRS model incorporates a weighting mechanism for conditional attributes. This mechanism enables the model to more accurately capture the contribution of each attribute to the decision attribute. A greedy searching algorithm is then used to select a subset of conditional attributes that exhibit both strong correlation and high dependency with the decision attribute, effectively reducing dimensionality and enhancing the overall performance of the attribute reduction process. Experimental results on sixteen datasets from the UCI machine learning repository demonstrate that the IWGBRS-based attribute reduction algorithm outperforms other popular methods. It offers improved classification accuracy and more effective attribute reduction with reduced randomness.</p>