More talks in the program:
17:30 - 18:00
Rank aggregation is the process of combining multiple individually ranked lists into one robust ranking (consensus ranking). Recently, the analysis of ranking data has been in significant interest of the machine learning community. Preference aggregation methods are used in computational social choice, multi-agent systems, meta-search engines e.g. rank web pages, and real world collective decisions, for instance election systems.
The focus of the project is to apply the versatile machine learning techniques into mechanisms of rank aggregation methods in order to predict the winner. The primary techniques mastered in this experimental study are learnability of voting rules by investigating machine learning algorithms as supervised classification tasks. With its different configurations, the set of agents (voters) have preferences (votes) over a set of alternatives (candidates). Taking as input the preferences of all agents (so-called profile), the mechanism framework determines the winner or an aggregated preference rank of all alternatives.
Clearly, the rank learning problem has a strong impact on identifying the election’s winner, as determining the winner in Kemeny’s voting scheme is NP-hard (over 4 candidates). The experimental study performs a comparison of several machine learning methods for Borda, Kemeny and Dodgson voting rules with the goal of establishing the best trade-offs between search time and performance.