FleBiC: Learning classifiers from high-dimensional biomedical data using discriminative biclusters with non-constant patterns

Abstract

The discovery of discriminative patterns from high-dimensional data offers the possibility to learn from informative subspaces and pattern-centric features, paving the way to associative classifiers. Despite the success achieved by associative classifiers, such as random forests or XGBoost, they generally neglect discriminative subspaces with non-constant coherencies. 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.This work assesses the impact of discriminative patterns with varying coherence and quality on associative classification. A novel classifier, FleBiC, is proposed as a result. 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. 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.