On Equivalence Relationships Between Classification and Ranking Algorithms
We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to ob...
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| Format: | Article |
| Language: | en_US |
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MIT Press
2012
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| Online Access: | http://hdl.handle.net/1721.1/71247 https://orcid.org/0000-0001-6541-1650 |
| _version_ | 1826211367017775104 |
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| author | Rudin, Cynthia Ertekin, Seyda |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Rudin, Cynthia Ertekin, Seyda |
| author_sort | Rudin, Cynthia |
| collection | MIT |
| description | We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; they can theoretically perform well when performance is measured either by a misclassification error or by a statistic of the ROC curve (such as the area under the curve). Specifically, we provide such an equivalence relationship between a generalization of Freund et al.'s RankBoost algorithm, called the "P-Norm Push," and a particular cost-sensitive classification algorithm that generalizes AdaBoost, which we call "P-Classification." We discuss and validate the potential benefits of this equivalence relationship, and perform controlled experiments to understand P-Classification's empirical performance. There is no established equivalence relationship for logistic regression and its ranking counterpart, so we introduce a logistic-regression-style algorithm that aims in between classification and ranking, and has promising experimental performance with respect to both tasks. |
| first_indexed | 2024-09-23T15:05:05Z |
| format | Article |
| id | mit-1721.1/71247 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:05:05Z |
| publishDate | 2012 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/712472022-10-02T00:27:47Z On Equivalence Relationships Between Classification and Ranking Algorithms Rudin, Cynthia Ertekin, Seyda Sloan School of Management Rudin, Cynthia Rudin, Cynthia Ertekin, Seyda We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; they can theoretically perform well when performance is measured either by a misclassification error or by a statistic of the ROC curve (such as the area under the curve). Specifically, we provide such an equivalence relationship between a generalization of Freund et al.'s RankBoost algorithm, called the "P-Norm Push," and a particular cost-sensitive classification algorithm that generalizes AdaBoost, which we call "P-Classification." We discuss and validate the potential benefits of this equivalence relationship, and perform controlled experiments to understand P-Classification's empirical performance. There is no established equivalence relationship for logistic regression and its ranking counterpart, so we introduce a logistic-regression-style algorithm that aims in between classification and ranking, and has promising experimental performance with respect to both tasks. National Science Foundation (U.S.) (grant number IIS-1053407) 2012-06-28T13:37:24Z 2012-06-28T13:37:24Z 2011-10 2011-08 Article http://purl.org/eprint/type/JournalArticle 1532-4435 1533-7928 http://hdl.handle.net/1721.1/71247 Seyda Ertekin and Cynthia Rudin "On Equivalence Relationships Between Classification and Ranking Algorithms" Journal of Machine Learning Research 12 (2011). https://orcid.org/0000-0001-6541-1650 en_US http://jmlr.csail.mit.edu/papers/v12/ertekin11a.html Journal of Machine Learning Research Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press MIT Press |
| spellingShingle | Rudin, Cynthia Ertekin, Seyda On Equivalence Relationships Between Classification and Ranking Algorithms |
| title | On Equivalence Relationships Between Classification and Ranking Algorithms |
| title_full | On Equivalence Relationships Between Classification and Ranking Algorithms |
| title_fullStr | On Equivalence Relationships Between Classification and Ranking Algorithms |
| title_full_unstemmed | On Equivalence Relationships Between Classification and Ranking Algorithms |
| title_short | On Equivalence Relationships Between Classification and Ranking Algorithms |
| title_sort | on equivalence relationships between classification and ranking algorithms |
| url | http://hdl.handle.net/1721.1/71247 https://orcid.org/0000-0001-6541-1650 |
| work_keys_str_mv | AT rudincynthia onequivalencerelationshipsbetweenclassificationandrankingalgorithms AT ertekinseyda onequivalencerelationshipsbetweenclassificationandrankingalgorithms |