Explanation-aware feature selection using symbolic time series abstraction: Approaches and experiences in a petro-chemical production context

For supporting interpretation, assessment and application of data mining models, explanation-aware methods are crucial. This paper presents an approach for explanation-aware feature selection and assessment using symbolic abstractions of time series. For that, we utilize the symbolic approximate aggregation (SAX) method for data abstraction to be implemented into data mining models. We investigate several approaches and discuss experiences in the context of petro-chemical production.