基于极少特征进行机器学习脉冲星检测
Pulsars Detection by Machine Learning with Very Few Features
AI总结：
本研究提出基于网格搜索和递归特征消除的特征选择算法，在HTRU-S数据上仅用2-3个特征即实现99%召回率和低误报率，显著提升脉冲星检测性能。
摘要：
在现代巡天数据量呈指数级增长的背景下，研究基于机器学习（ML）方法检测脉冲星的方案是一个活跃课题。为了提高检测性能，应专门研究输入到ML模型中的特征。在现有的基于ML方法的脉冲星检测研究中，主要有两类特征设计：经验特征和统计特征。然而，由于多个特征的组合效应，现有特征中存在一些冗余甚至无关的成分，这会降低脉冲星检测模型的准确性。因此，从一组可用的候选特征中选择相关特征子集至关重要，这被称为特征选择。在这项工作中，提出了两种特征选择算法——网格搜索（GS）和递归特征消除（RFE）——通过去除冗余和无关特征来提高检测性能。这些算法在南方高时间分辨率大学巡天（HTRU-S）上使用五种脉冲星检测模型进行了评估。实验结果验证了我们提出的特征选择算法的有效性和效率。通过GS，仅使用两个特征的模型达到了高达99%的召回率和低至0.65%的假阳性率（FPR）；通过RFE，另一个仅使用三个特征的模型在脉冲星候选体分类中达到了99%的召回率和0.16%的FPR。此外，这项工作还研究了所需特征的数量以及我们的模型误分类的脉冲星。
英文摘要：
It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features, which can reduce the accuracy of a pulsar detection model. Therefore, it is essential to select a subset of relevant features from a set of available candidate features and known as {\itshape feature selection.} In this work, two feature selection algorithms ----\textit{Grid Search} (GS) and \textit{Recursive Feature Elimination} (RFE)---- are proposed to improve the detection performance by removing the redundant and irrelevant features. The algorithms were evaluated on the Southern High Time Resolution University survey (HTRU-S) with five pulsar detection models. The experimental results verify the effectiveness and efficiency of our proposed feature selection algorithms. By the GS, a model with only two features reach a recall rate as high as 99\% and a false positive rate (FPR) as low as 0.65\%; By the RFE, another model with only three features achieves a recall rate 99\% and an FPR of 0.16\% in pulsar candidates classification. Furthermore, this work investigated the number of features required as well as the misclassified pulsars by our models.
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