In this paper, we present the paradigm of Language-Driven Engineering (LDE), which is characterized by its unique support for division of labour on the basis of Domain-Specific Languages (DSLs) targeting different stakeholders. LDE allows the involved stakeholders, including the application experts, to participate in the system development and evolution process using dedicated DSLs, while at the same time establishing new levels of reuse that are enabled by powerful model transformations and code generation. Technically, the interplay between the involved DSLs is realized in a service-oriented fashion. This eases a product line approach and system evolution by allowing to introduce and exchange entire DSLs within corresponding Mindset-Supporting Integrated Development Environments (mIDEs). The impact of this approach is illustrated along the development and evolution of a profilebased email distribution system. Here we do not want to emphasize the precise choice of DSLs, but rather the flexible DSL-based modularization of the development process, which allows one to freely introduce and exchange DSLs as needed to optimally capture the mindsets of the involved stakeholders.
Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise the outcome of their predictions. However, this comes at a cost: it is increasingly difficult to understand why a Random Forest made a specific choice, and its running time for classification grows linearly with the size (number of trees). In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram which has the following two effects: (1) minimal, sufficient explanations for Random Forest-based classifications can be obtained by means of a simple three step reduction, and (2) the running time is radically improved. In fact, our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.
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