Abstract A pulsar is a rapidly rotating neutron star and transmits periodic oscillations of power to the earth. We introduce a novel method for pulsar candidate classification. The method contains two major steps: (1) make strong representations for pulsar candidate in the image domain by extracting deep features with the deep convolutional generative adversarial Networks (DCGAN) and (2) develop a classifier defined by multilayer perceptron (MLP) neural networks trained with pseudoinverse learning autoencoder (PILAE) algorithm. We utilized the synthetic minority over-sampling technique (SMOTE) to handle the imbalance in the dataset. We report a variety of measure scores from the output of the PILAE method on datasets utilized in the experiments. The PILAE training process does not have to determine the learning control parameters or indicate the number of hidden layers. Therefore, the PILAE classifier can fulfil superior execution in terms of training effectiveness and accuracy. Empirical results from the high time resolution universe (HTRU) mid-latitude dataset, MNIST dataset and CIFAR-10 have demonstrated that the presented framework achieves excellent results with other models and reasonably low complexly.

中文翻译：

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结合 DCGAN 和 PILAE 算法从不平衡脉冲星数据中学习

摘要 脉冲星是一种快速自转的中子星，向地球传递周期性振荡的能量。我们介绍了一种新的脉冲星候选分类方法。该方法包含两个主要步骤：(1) 通过使用深度卷积生成对抗网络 (DCGAN) 提取深层特征，在图像域中对候选脉冲星进行强表示；(2) 开发由多层感知器 (MLP) 神经网络定义的分类器用伪逆学习自动编码器 (PILAE) 算法训练。我们利用合成少数过采样技术（SMOTE）来处理数据集中的不平衡。我们报告了来自 PILAE 方法对实验中使用的数据集的输出的各种测量分数。PILAE 训练过程不必确定学习控制参数或指示隐藏层数。因此，PILAE 分类器可以在训练有效性和准确性方面实现卓越的执行。来自高时间分辨率宇宙 (HTRU) 中纬度数据集、MNIST 数据集和 CIFAR-10 的实证结果表明，所提出的框架与其他模型一起取得了出色的结果，并且复杂度相当低。