Metaheuristics for Query Synthesis in One-Class Active Learning

Aus SDQ-Institutsseminar
Vortragende(r) Philipp Schüler
Vortragstyp Bachelorarbeit
Betreuer(in) Adrian Englhardt
Termin Fr 5. April 2019
Kurzfassung Active learning describes the topic of a human operator helping with the machine learning process. By asking for a classification of queries, the precision of the machine learning algorithm is increased. Existing research focuses on the idea of using a pool of unlabelled data points or use multiple class cases. We have developed a framework, that allows to synthesize a query in the one-class setting without requiring unlabelled data points. The optimal query is the data point with the highest amount of information. The amount of information for a specific data point is given by the informativeness function. We have created a framework to use metaheuristics to find the maximum of the informativeness function and thus determine the optimal query. We have also conducted experiments to provide a general guideline for the usage of metaheuristics in one-class query synthesis.