Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection
| Autor: | Wellington Santos Martins, Marcos André Gonçalves, Sergio Canuto, Daniel Xavier de Sousa, Thierson Couto Rosa |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Evolutionary algorithm Feature selection 02 engineering and technology Space (commercial competition) Risk sensitive Machine learning computer.software_genre General Business Management and Accounting Computer Science Applications Reduction (complexity) Feature (computer vision) 020204 information systems Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Learning to rank Artificial intelligence business computer Information Systems |
| Zdroj: | ACM Transactions on Information Systems. 37:1-34 |
| ISSN: | 1558-2868 1046-8188 |
| DOI: | 10.1145/3300196 |
| Popis: | Learning to Rank (L2R) is one of the main research lines in Information Retrieval. Risk-sensitive L2R is a sub-area of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly in a few but important queries (e.g., medical or legal queries). One way of reducing risk in learned models is by selecting and removing noisy, redundant features, or features that promote some queries to the detriment of others. This is exacerbated by learning methods that usually maximize an average metric (e.g., mean average precision (MAP) or Normalized Discounted Cumulative Gain (NDCG)). However, historically, feature selection (FS) methods have focused only on effectiveness and feature reduction as the main objectives. Accordingly, in this work, we propose to evaluate FS for L2R with an additional objective in mind, namely risk-sensitiveness . We present novel single and multi-objective criteria to optimize feature reduction, effectiveness, and risk-sensitiveness, all at the same time. We also introduce a new methodology to explore the search space, suggesting effective and efficient extensions of a well-known Evolutionary Algorithm (SPEA2) for FS applied to L2R. Our experiments show that explicitly including risk as an objective criterion is crucial to achieving a more effective and risk-sensitive performance. We also provide a thorough analysis of our methodology and experimental results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|