Listwise approaches based on feature ranking discovery
Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing...
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Veröffentlicht in: | Frontiers of Computer Science 2012-12, Vol.6 (6), p.647-659 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms. |
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ISSN: | 1673-7350 2095-2228 1673-7466 2095-2236 |
DOI: | 10.1007/s11704-012-1170-7 |