Search Results (2)
Explainable machine learning techniques for applications in life sciences
As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …
Knowledge discovery in a service-oriented data mining environment
The thesis addresses the development of novel knowledge discovery scenarios in a modern data mining platform by utilising principles of service-oriented architecture with web services, interactive scientific workflows, knowledge discovery ontologies and automated construction of data mining workflows. We present the developed Orange4WS platform which upgrades Orange, a mature open-source …