A feature selection method based on minimum redundancy maximum relevance for learning to rank

Mehrnoush Barani Shirzad, M. Keyvanpour
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引用次数: 19

Abstract

Learning to rank has considered as a promising approach for ranking in information retrieval. In recent years feature selection for learning to rank introduced as a crucial issue. Reducing the feature set by removing irrelevant and redundant features can improve the prediction performance. In this paper we address the problem of filter feature selection for ranking. We propose to apply minimum redundancy maximum relevance (mRMR) method that select feature subset based on importance of features and similarity between them. We reweight the component of mRMR to balance between importance and similarity. We apply two methods for measuring the similarity between features and two methods for evaluating importance. Experimental results on two standard datasets from Letor demonstrate that the proposed algorithm 1)outperform two stateof- the-art learning to rank algorithms in term of accuracy, 2) learn a more spars model compared to a feature selection model for ranking.
一种基于最小冗余最大关联的特征选择方法用于排序学习
学习排序被认为是一种很有前途的信息检索排序方法。近年来,特征选择作为学习排序的一个关键问题被引入。通过去除不相关和冗余的特征来减少特征集,可以提高预测性能。本文研究了用于排序的滤波器特征选择问题。我们提出应用最小冗余最大相关性(mRMR)方法,根据特征的重要性和特征之间的相似性选择特征子集。我们重新加权mRMR的组成部分,以平衡重要性和相似性。我们采用两种方法来测量特征之间的相似性和两种方法来评估重要性。在两个来自Letor的标准数据集上的实验结果表明,该算法1)在准确性方面优于两种最先进的学习排序算法;2)与特征选择模型相比,学习了一个更精细的模型进行排序。
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