Title: Rule-based recommender for feature engineering in big data
Other Titles: Regelbasierte Empfehlungen für Feature Engineering in großen Datenmengen
Language: English
Authors: Lepadat, Mihai-Alexandru 
Qualification level: Diploma
Advisor: Tjoa, A Min  
Assisting Advisor: Knees, Peter  
Issue Date: 2019
Number of Pages: 72
Qualification level: Diploma
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-driven 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.
Keywords: Feature Engineering; Recommender; Machine Learning; Apache Spark; Ontology
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-125384
Library ID: AC15381183
Organisation: E194 - Institut für Information Systems Engineering 
Publication Type: Thesis
Appears in Collections:Thesis

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