Semantische Suche

discovery and allows strategic decisions based on this information. Many dependency estimation algorithms require a large amount of data for a good estimation. But data can be expensive, as an example experiments in material sciences, consume material and take time and energy. As we have the challenge of expensive data collection, algorithms need to be data efficient. But there is a trade-off between the amount of data and the quality of the estimation. With a lack of data comes an uncertainty of the estimation. However, the algorithms do not always quantify this uncertainty. As a result, we do not know if we can rely on the estimation or if we need more data for an accurate estimation. In this bachelor’s thesis we compare different state-of-the-art dependency estimation algorithms using a list of criteria addressing the above-mentioned challenges. We partly developed the criteria our self as well as took them from relevant publications. Many of the existing criteria where only formulated qualitative, part of this thesis is to make these criteria measurable quantitative, where possible, and come up with a systematic approach of comparison for the rest. We also conduct a quantitative analysis of the dependency estimation algorithms by experiment on well-established and representative data sets that performed well in the qualitative analysis.

However, these approaches assume the existence of high quality domain knowledge and do not acknowledge issues stemming from low quality domain knowledge. It is thus unclear what low quality domain knowledge in the context of Domain Knowledge Guided Machine Learning looks like and what its causes are. Further it is not clearly understood what the impact of low quality domain knowledge on the machine learning task is and what steps can be taken to improve the quality in this context.

In this Thesis we describe low quality domain knowledge and show examples of such knowledge in the context of a sequential prediction task. We further propose methods for identifying low quality domain knowledge in the context of Domain Knowledge Guided Machine Learning and suggest approaches for improving the quality of domain knowledge in this context.