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The paper proposes a feature-binarisation method for Tsetlin Machines, a logic-based, interpretable learner, applied to hyperspectral classification. Its key claim is that contrast-based Boolean predicates between spectral intervals outperform standard per-band binarisation across five datasets. The gains are shown with Bayesian and rank-based analyses, but the evidence is limited to classical tabular-style classifiers on a handful of benchmarks, so readers should check how it generalises before assuming broader applicability.
Spectral Haar Binarizer: Compact Boolean Contrast Representations for Hyperspectral Classification
arXiv:2610.41478v1 Announce Type: new Abstract: Tsetlin Machines offer an interpretable, hardware-friendly alternative to conventional machine learning models, but applying them to continuous hyperspectral measurements requires converting spectral signals into Boolean representations. Standard binarisation methods typically discretise each spectral band independently, potentially discarding important relationships between neighbouring wavelengths. In this paper, we introduce the Spectral Haar Binarizer (SHB), a contrast-based spectral representation method that constructs Boolean predicates from differences between selected spectral intervals. Inspired by Haar-like contrast features, the method uses an AdaBoost-style selection procedure to obtain a compact set of discriminative spectral predicates. We evaluate SHB on five heterogeneous hyperspectral datasets covering forensic blood detection, agricultural and urban remote sensing, and rainforest tree-species classification. Across binary and multiclass tasks, SHB consistently outperforms standard per-band binarisation strategies and provides the strongest or near-strongest results across classifiers. In binary tasks, TM-SHB achieves the best average performance, while RF-SHB is strongest in multiclass settings. Bayesian and rank-based analyses confirm that SHB's advantage is robust across datasets and not driven by isolated cases. The results show that representing continuous hyperspectral measurements is a central processing step for efficient classification. That contrast-based Boolean features provide an effective bridge between information-rich sensor measurements and compact, logic-based processing.
Abstract:Tsetlin Machines offer an interpretable, hardware-friendly alternative to conventional machine learning models, but applying them to continuous hyperspectral measurements requires converting spectral signals into Boolean representations. Standard binarisation methods typically discretise each spectral band independently, potentially discarding important relationships between neighbouring wavelengths. In this paper, we introduce the Spectral Haar Binarizer (SHB), a contrast-based spectral representation method that constructs Boolean predicates from differences between selected spectral intervals. Inspired by Haar-like contrast features, the method uses an AdaBoost-style selection procedure to obtain a compact set of discriminative spectral predicates. We evaluate SHB on five heterogeneous hyperspectral datasets covering forensic blood detection, agricultural and urban remote sensing, and rainforest tree-species classification. Across binary and multiclass tasks, SHB consistently outperforms standard per-band binarisation strategies and provides the strongest or near-strongest results across classifiers. In binary tasks, TM-SHB achieves the best average performance, while RF-SHB is strongest in multiclass settings. Bayesian and rank-based analyses confirm that SHB's advantage is robust across datasets and not driven by isolated cases. The results show that representing continuous hyperspectral measurements is a central processing step for efficient classification. That contrast-based Boolean features provide an effective bridge between information-rich sensor measurements and compact, logic-based processing.
Machine Learning (cs.LG)
arXiv:2610.41478 [cs.LG]
(or arXiv:2610.41478v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.41478 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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