EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.
IGBO uses bi-objective optimization with a CLT-derived DAG for feature hierarchies, Temporal Integrated Gradients, and a geometric projection to align accuracy and explainability while proving convergence to Pareto-stationary points.
citing papers explorer
-
EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
-
When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.
-
Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability
IGBO uses bi-objective optimization with a CLT-derived DAG for feature hierarchies, Temporal Integrated Gradients, and a geometric projection to align accuracy and explainability while proving convergence to Pareto-stationary points.