Active learning of soft rules for system modelling

Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented method estimates the utility of new experiments based on the knowledge represented by the existing rulebase. An extended rule format allows to deal with uncertainty. Experiments with different artificial system functions show that the presented method improves the model quality respectively decreases the number of experiments needed to reach a specific level of performance

Consultant-2: pre- and post processing of machine learning applications.

The knowledge acquisition bottleneck in the development of large knowledge-based applications has not yet been resolved. One approach which has been advocated is the systematic use of Machine Learning (ML) techniques. However, ML technology poses difficulties to domain experts and knowledge engineers who are not familiar with it. This paper discusses Consultant-2, a system which makes a first step towards providing system support for a pre- and post-processing methodology where a cyclic process of experiments with an ML tool, its data, data description language and parameters attempts to optimize learning performance. Consultant-2 has been developed to support the use of Machine Learning Toolbox (MLT), an integrated architecture of 10 ML tools, and has evolved from a series of earlier systems. Consultant-0 and Consultant-1 had knowledge only about how to choose an ML algorithm based on the nature of the domain data. Consultant-2 is the most sophisticated. It, additionally, has knowledge about how ML experts and domain experts pre-process domain data before a run with the ML algorithm, and how they further manipulate the data and reset parameters after a run of the selected ML algorithm, to achieve a more acceptable result. How these several KBs were acquired and encoded is described. In fact, this knowledge has been acquired by interacting both with the ML algorithm developers and with domain experts who had been using the MLT toolbox on real-world tasks. A major aim of the MLT project was to enable a domain expert to use the toolbox directly; i.e. without necessarily having to involve either a ML specialist or a knowledge engineer. Consultant's principal goal was to provide specific advice to ease this process