多层克隆选择的排序学习方法研究
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摘要
提出一种基于多层克隆选择的排序学习方法,将克隆选择理论应用于学习排序函数,对传统克隆选择进行改进,使用一种按层变异的方法和一种多层的克隆选择架构逐步进化抗体库,最终得到最优的排序函数。提出的方法在LETOR基准数据集进行评价,结果表明了算法在多数情况下优于基线算法。
A learning to rank method based on multi-layer clonal selection is presented.The clonal selection theory is applied to learn ranking functions and an improvement is applied to the traditional clonal selection algorithm.A layered mutation method and a multi-layer clonal selection architecture are used to evolve antibody repertoire and get the optimal ranking function.Proposed method is evaluated on the LETOR data collection,and results show that the proposed algorithm is more effective than baseline algorithms in most cases.
A learning to rank method based on multi-layer clonal selection is presented.The clonal selection theory is applied to learn ranking functions and an improvement is applied to the traditional clonal selection algorithm.A layered mutation method and a multi-layer clonal selection architecture are used to evolve antibody repertoire and get the optimal ranking function.Proposed method is evaluated on the LETOR data collection,and results show that the proposed algorithm is more effective than baseline algorithms in most cases.
引文
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[13]WANG SHUAIQIANG,MA JUN,HE QIANG.An immune programming-based ranking function discovery approach for effective information retrieval[J].Expert Systems with Applications,2010,37(8):5863-5871.
[2]ZHANG X,HE B,LUO T J.Training query filtering for semi-supervised learning to rank with pseudo labels[J].World Wide Web,2015:1-32.
[3]LIU T Y,XU J,QIN T,et al.Letor:benchmark dataset for research on learning to rank for information retrieval[C]∥Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval.2007:3-10.
[4]HOFMANN K.Fast and reliable online learning to rank for information retrieval[J].ACM SIGIR Forum,2013,47(2):140.
[5]SCHUTH A,HOFMANN K,WHITESON S,et al.Lerot:An online learning to rank framework[C]∥ACM.Proceedings of the 2013 Workshop on Living Labs for Information Retrieval Evaluation,2013:23-26.
[6]MOREIRA C,CALADO P,MARTINS B.Learning to rank academic experts in the DBLP dataset[J].Expert Systems,2015,32(4):477-493.
[7]RAMADHAN A,KHODRA M L.Learning to rank for top mobile games prediction[C]∥IEEE.Information and Communication Technology(ICoICT),2014:53-58.
[8]BINKLEY D,LAWRIE D.Learning to rank improves IR in SE[C]∥IEEE.Software Maintenance and Evolution(ICSME),2014:441-445.
[9]奚凌然,王小平,XiLingran,等.一种结合LPA半监督学习的排序学习算法[J].计算机应用与软件,2016,33(1):286-290.XI L R,WANG X P,et al.A ranking learning algorithm combining semi-supervised learning of LPA[J].Computer Applications and Software,2016,33(1):286-290.
[10]ZHAO P,WU O,GUO L,et al.Deep learning-based learning to rank with ties for image re-ranking[C]∥IEEE.International Conference on Digital Signal Processing,2017:452-456.
[11]XU J,LIU T Y,LU M,et al.Directly optimizing evaluation measures in learning to rank[C]∥ACM.Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Informatiosn Retrieval,2008:107-114.
[12]LIN J Y,YEH J Y,LIU C C.Learning to rank for information retrieval using layered multi-population genetic programming[C]∥IEEE.Computational Intelligence and Cybernetics(CyberneticsCom),2012:45-49.
[13]WANG SHUAIQIANG,MA JUN,HE QIANG.An immune programming-based ranking function discovery approach for effective information retrieval[J].Expert Systems with Applications,2010,37(8):5863-5871.