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M. Lepadat:
"Rule-based Recommender for Feature Engineering in Big Data";
Supervisor: A. Tjoa, E. Kiesling, P. Knees; Techniche Universität Wien, 2019; final examination: 2019-06-05.

Feature engineering is of high importance for the success of many machine learning algorithms and requires domain-specific knowledge. Generally, this knowledge is only familiar to domain experts or incorporated into programs. We developed a knowledge- drive approach to support users during feature engineering and implemented a software application to evaluate this approach. The knowledge is represented in Web Ontology Language (OWL) and its main purpose is to offer the user a flexible way to tackle domain-specific datasets by building a reusable and comprehensible knowledge base. A semantic reasoner makes use of this knowledge to infer properties and provide users with recommendations. All data-related operations are performed in a scalable cluster computing engine backed up by Apache Spark. The evaluation is done on 6 freely available datasets from the domain of demographics. We were able to identify only a small fraction of recommendations that proved to be wrong.

Feature Engineering, Recommender, Machine Learning, Apache Spark, Ontology

https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-125384