Multiple Instance Learning (MIL) is a useful method for extracting information from Gigapixel images using weakly supervised learning. Traditionally, it was approached with the hypothesis of considering each instance as an independent and identically distributed entity. Recently, it has been suggested that correlations between different instances would yield better results for classification. In this study, we propose that by selecting features for enhanced correlation, we aim to identify optimal characteristics that capture tumor regions both locally and from distant areas within a Whole Slide Image (WSI). This approach is designed to improve classification performance significantly. The proposed method facilitates binary classification with enhanced insights into more aggressive tumor regions. We conducted various experiments on three different computational pathology problems out of which one is classification between normal tissue and tumor and other is between different types of tumor. We achieved better results than state-of-the-art methods for tumor sub-classification. The test accuracy for tumor sub-classification on the TCGA-NSCLC dataset was 88.03%, and on the TCGA-RCC dataset, it was 92.42%.