Responsible Credit Risk Assessment with Machine Learning and Knowledge Acquisition

Machine Learning and Knowledge Acquisition Acknowledgment Machine Learning and Knowledge Acquisition 4.3. Axiomatization

427 D. Fensel and R. Groenboom choose and the range constructs, cf. [7]). Otherwise, current operationalizations and axiomizations of ealgebras are restricted to the case of point-wise updates. A further difference to our approach is that MLPM provides a richer vocabulary to express control over the execution of transitions. The language DESIRE uses the notion of meta-layered compositional arch...

Knowledge Acquisition with a Knowledge-Intensive Machine Learning System

In this paper, we investigate the integration of knowledge acquisition and machine learning techniques. We argue that existing machine learning techniques can be made more useful as knowledge acquisition tools by allowing the expert to have greater control over and interaction with the learning process. We describe a number of extensions to FOCL (a multistrategy Horn-clause learning program) th...

Credit Risk Assessment using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications

Risk assessment of financial intermediaries is an area of renewed interest due to the financial crises of the 1980’s and 90’s. An accurate estimation of risk, and its use in corporate or global financial risk models, could be translated into a more efficient use of resources. One important ingredient to accomplish this goal is to find accurate predictors of individual risk in the credit portfol...

Research on Knowledge Representation , Machine Learning , and Knowledge Acquisition

Techniques for integrating machine learning with knowledge acquisition

Knowledge acquisition is frequently cited as the greatest bottleneck in the development of expert systems. Two primary approaches to knowledge acquisition are elicitation of knowledge from experts (traditional knowledge acquisition) and machine learning. Both approaches have strengths and weaknesses. Experts can draw upon practical experience and both domain specific and general knowledge. In a...