Efficient AUC Optimization for Information Ranking Applications
| Autor: | Sean Welleck |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Generalization
business.industry Computer science 05 social sciences Word error rate 02 engineering and technology Machine learning computer.software_genre Regression Ranking (information retrieval) ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Relevance (information retrieval) Learning to rank Artificial intelligence 0509 other social sciences 050904 information & library sciences business Area under the roc curve computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319306704 ECIR |
| Popis: | Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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