Expert-in-the-Loop Machine Learning for Robust Startup Classification: A Hybrid Approach to Low-Signal Data Classification

Abstract

The expert-in-the-loop machine learning framework addresses the challenges of startup classification using limited and ambiguous data sources. By incorporating human expertise in data labeling and post-processing stages, the system demonstrates improved model precision and reliability in enterprise-grade classification tasks. The combination of consensus-based expert labeling with automated machine learning pipelines creates a scalable and interpretable solution for high-value business decisions. The framework successfully balances automation with human insight, enabling more accurate startup detection while maintaining transparency and trust in model outputs. The integration of domain expertise throughout the classification pipeline has proven particularly effective in handling edge cases and evolving market conditions, while the systematic approach to knowledge capture ensures consistent performance across different industry sectors. This hybrid approach not only enhances classification accuracy but also provides stakeholders with clear decision rationales and maintains adaptability to changing business environments.