A Reproducible Benchmark for Gas Sensor Array Classification: From FE-ELM to ROCKET and TS2I-CNNs

Area of Science:

• Machine Learning
• Sensor Technology
• Data Science

Background:

• Classifying low-concentration Gas Sensor Array (GSA) data presents significant challenges due to low signal-to-noise ratio (SNR), sensor heterogeneity, drift, and limited sample sizes.
• Existing methods often struggle to achieve robust performance under these adverse conditions, necessitating novel approaches and comprehensive benchmarking.

Purpose of the Study:

• To benchmark various time-series classification methods, including time-series-to-image (TS2I) Convolutional Neural Networks (CNNs), against established baselines for low-concentration GSA data.
• To provide a reproducible and fair comparison of different classification strategies to guide model selection in electronic nose (e-nose) applications.

Main Methods:

• Reproduced a strong FE-ELM baseline and compared it with vector baselines, traditional time-series classifiers (TCN, MiniROCKET), and TS2I-CNNs.
• Utilized the GSA-LC and GSA-FM datasets with a rigorous 20x5 repeated stratified cross-validation (n=100) for robust evaluation.
• Assessed performance using accuracy and Macro-F1 scores, employing paired t-tests with Holm-Bonferroni correction for statistical significance.

Main Results:

• The ROCKET time-series classifier achieved the highest accuracy on both GSA-FM (0.9721 ± 0.0480) and GSA-LC (0.9578 ± 0.0433) datasets, significantly outperforming TCN and MiniROCKET (p < 0.05).
• Among image-based models, CNN-RP demonstrated the most robustness, while CNN-GASF showed limitations, particularly on the GSA-LC dataset.
• RGB fusion strategies and transfer learning with ResNet-18 did not offer consistent advantages, indicating dataset-dependency and limited generalizability.

Conclusions:

• ROCKET is the top-performing model for classifying low-concentration GSA data, offering superior accuracy and reliability.
• CNN-RP serves as the most viable TS2I alternative for these challenging conditions, providing a robust image-based approach.
• The study establishes a reproducible benchmark, offering practical guidance for model selection in e-nose systems and clarifying the capabilities of TS2I methods.