Multi-objective Feature Weighting: A Dual Phase Evolutionary Approach for Cancer Classification

Digital pathology has transformed cancer diagnosis through computational analysis of whole slide images (WSIs). However, the high-dimensional nature of extracted features presents a major challenge for accurate classification, necessitating an effective feature weighting strategy. In this work, we propose a novel two-phase multi-objective feature weighting framework for cancer classification. In the first phase, we introduce a new frequency-based feature selection method to reduce the search space by eliminating redundant and irrelevant features. In the second phase, we apply a novel feature weighting approach that integrates feature correlation in the initialization process, increasing the likelihood of selecting highly correlated features together. Additionally, features with weights below a predefined threshold are zeroed out, further refining the selection while enhancing classification accuracy. Our experimental results on features extracted from TCGA histopathology images show that the proposed approach outperforms direct feature weighting on all features. By leveraging correlation-aware initialization and frequency-based selection, our method effectively optimizes feature representation, leading to improved cancer classification performance in digital pathology.