Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for this task. Under the general framework of simulated annealing, we explore various neighborhood selection strategies and temperature cooling schemes. We further introduce a new hyper-parameter called the progress parameter that can effectively be used to traverse the search space. Our algorithms are evaluated on five publicly benchmark datasets of learning-to-rank. For a better validation, we also compare the simulated annealing-based feature selection algorithm with another effective meta-heuristic algorithm, namely local beam search. Extensive experimental results shows the efficacy of our proposed models.

翻译：排序学习是监督机器学习的一个应用领域。由于特征选择已被发现能普遍提高学习模型的准确性，因此探究其在排序学习领域中的过程具有重要研究价值。本研究针对这一任务，探索了主流元启发式方法——模拟退火算法的应用。在模拟退火通用框架下，我们研究了多种邻域选择策略和温度冷却方案，并引入了一个名为"进度参数"的新超参数，可有效用于遍历搜索空间。我们的算法在五个公开的排序学习基准数据集上进行了评估。为增强验证可靠性，我们还将基于模拟退火的特征选择算法与另一种有效的元启发式算法——局部束搜索进行了对比。大量实验结果表明了我们提出模型的效能。