What is Silas?
Silas, is a generic data mining and predictive analytics software toolkit built upon advanced machine learning, automated reasoning and artificial intelligence techniques. It deals with any type of structured data and performs tasks such as classification, regression, segmentation, anomaly detection, prediction, and more.
This paper introduces Silas as a high-performance machine learning tool, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advan- tage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions.
Performant
Silas implements novel tree-based learning algorithms that yield high predictive performance when dealing with structured data.
Fast and Efficient
Silas adopts various high-performance computing techniques to ensure that the computation is fast and memory-efficient. Silas is built to handle big data and it can be deployed on high performance clusters.
Dependable
Silas uses formal verification techniques to mathematically verify that the prediction model satisfies user specifications. Further, user specifications can be enforced during the training phase.
Insightful
Silas adopts the latest automated reasoning techniques to reason about the predictive model and provides insights on the rationale behind the decision-making.