Norbert Kerekes (LinkedIn)
Multi-label classification is a machine learning task seldom mentioned, considering how prevalent the problem is in everyday life.
The thesis is about this problem, aiming to overview and compare algorithms suited to solve multi-label problems. The most important representatives of the two greater algorithm families (problem transformation and adaptive algorithm methods) are presented in a text classification problem. The database contains depression-related online forum entries categorized by the biopsychosocial model.