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<p>High-dimensional data in agriculture and remote sensing suffer from the curse of dimensionality, leading to poor model performance and high computational cost. Feature selection mitigates this by removing irrelevant and redundant features while preserving discriminative power. We propose Fuzzy Hypergraph Feature Association Map (FH-FAM), a novel supervised feature selection method that uses fuzzy hypergraphs to model higher-order feature interactions and uncertainty. It computes multi-way normalized mutual information for relevance, multi-way correlation for redundancy, applies sigmoidal and gamma fuzzy membership functions, constructs weighted fuzzy hypergraphs, and selects an optimized subset via maximal independent set after three-stage refinement. FH-FAM was evaluated on 15 public datasets (food/agriculture and remote sensing domains) using Random Forest classification (80:20 train–test split). Compared to FFAMFS, CFS, DSCA, FCFB, FROT, and ABESS, FH-FAM achieved the highest mean accuracy (81.43%) and mean feature reduction (89.28%), with competitive execution time. The method excels in capturing complex multi-feature dependencies and uncertainty typical in crop classification, seed variety identification, and satellite-based land cover/environmental monitoring tasks.</p>

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Fuzzy Hypergraph Feature Association Map for High-Dimensional Feature Selection in Agriculture and Remote Sensing

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Rajashik Datta,
  • Sanjan Baitalik,
  • Sruti Das Choudhury,
  • Arup Kumar Chattopadhyay,
  • Amit Kumar Das

摘要

High-dimensional data in agriculture and remote sensing suffer from the curse of dimensionality, leading to poor model performance and high computational cost. Feature selection mitigates this by removing irrelevant and redundant features while preserving discriminative power. We propose Fuzzy Hypergraph Feature Association Map (FH-FAM), a novel supervised feature selection method that uses fuzzy hypergraphs to model higher-order feature interactions and uncertainty. It computes multi-way normalized mutual information for relevance, multi-way correlation for redundancy, applies sigmoidal and gamma fuzzy membership functions, constructs weighted fuzzy hypergraphs, and selects an optimized subset via maximal independent set after three-stage refinement. FH-FAM was evaluated on 15 public datasets (food/agriculture and remote sensing domains) using Random Forest classification (80:20 train–test split). Compared to FFAMFS, CFS, DSCA, FCFB, FROT, and ABESS, FH-FAM achieved the highest mean accuracy (81.43%) and mean feature reduction (89.28%), with competitive execution time. The method excels in capturing complex multi-feature dependencies and uncertainty typical in crop classification, seed variety identification, and satellite-based land cover/environmental monitoring tasks.