The paper “FleBiC: Learning classifiers from high-dimensional biomedical data using discriminative biclusters with non-constant patterns” co-authored by Sara C. Madeira has been published in Pattern Recognition, a top-ranked journal (IF: 7.197; h5-index: 85).
Research on biclustering has, for two decades, highlighted the role of non-constant patterns in biomedical domains, including additive and order-preserving patterns. Still, their relevance for classification remains unexplored because current associative classifiers (such as random forests or XGBoost) generally neglect discriminative subspaces with non-constant coherencies. In this paper, the impact of discriminative patterns with varying coherence and quality on associative classification is assessed and a novel classifier, FleBiC, is proposed. FleBiC extends pattern-based biclustering with principles to match observations against non-constant and noise-tolerant patterns, address generalization difficulties, minimize scarcity of matches, support class disjunctions, and offer statistical guarantees. The achieved results on biological and clinical data highlight the role of non-constant patterns, specially order-preserving patterns, for improving the performance of state-of-the-art classifiers.
The paper is available here.