Designing generalisation evaluation function through human-machine dialogue
This addresses a specific bottleneck in automated generalisation systems for users needing better evaluation metrics, but it is incremental as it builds on existing methods.
The paper tackles the problem of automatically evaluating generalised data by proposing a human-machine dialogue approach to revise imperfect evaluation functions, with an experiment on buildings showing significant improvements.
Automated generalisation has known important improvements these last few years. However, an issue that still deserves more study concerns the automatic evaluation of generalised data. Indeed, many automated generalisation systems require the utilisation of an evaluation function to automatically assess generalisation outcomes. In this paper, we propose a new approach dedicated to the design of such a function. This approach allows an imperfectly defined evaluation function to be revised through a man-machine dialogue. The user gives its preferences to the system by comparing generalisation outcomes. Machine Learning techniques are then used to improve the evaluation function. An experiment carried out on buildings shows that our approach significantly improves generalisation evaluation functions defined by users.