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首页> 外文会议>IEEE International Conference on Green Computing and Communications;IEEE International Conference on Internet of Things;IEEE International Conference on Cyber, Physical and Social Computing;IEEE International Conference on Smart Data;IEEE Congress on Cybermatics >A feature selection algorithm for multilayer perceptron based on simultaneous two-sample representation
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A feature selection algorithm for multilayer perceptron based on simultaneous two-sample representation

机译：基于同时两个样本表示的多层Perceptron特征选择算法

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摘要

Classification is one of the hot topics of machine learning domains, its main task is to learn a classification model from training data and predict the labels of unknown samples. To date, many classification models have been proposed and are widely used in various realworld applications, e.g., naive Bayes (NB), logistic regression (LR), support vector machine (SVM) have been successfully employed in spam recognition, bank loan credit scoring and network rumor recognition, respectively. Imbalance learning is an important branch of classification task in machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning. To alleviate the issues of data explosion and feature selection for a multilayer perceptron with simultaneous two-sample representation, in this paper, we propose a novel feature selection method based on the pairwise samples distance constraint, which considers the class labels of paired samples, select the features which push two similar samples closer together and pull two different samples farther apart. Finally, we conduct experiments on four high-dimensional DNA microarray datasets. The experimental results demonstrate that our proposed algorithms outperform some state-of-the-art algorithms in terms of F-measure and G-mean.
机译：分类是机器学习域的热门话题之一，其主要任务是从训练数据中学习分类模型，并预测未知样本的标签。迄今为止，已经提出了许多分类模型，并且广泛用于各种RealWorld应用程序，例如Naive Bayes（NB），Logistic回归（LR），支持向量机（SVM）已经成功地在垃圾邮件识别中成功使用，银行贷款信贷得分和网络谣言识别。不平衡学习是机器学习域中分类任务的重要分支。数据级，算法级和集合解决方案是迄今为止提出的三种主要方法，以解决不平衡学习。为了缓解多层Perceptron的数据爆炸和特征选择的问题，在本文中，我们提出了一种基于成对样本距离约束的新型特征选择方法，这考虑了配对样本的类标签，选择将两个类似样品更靠近的特征，并拉两个不同的样品进一步相距。最后，我们对四个高维DNA微阵列数据集进行实验。实验结果表明，我们所提出的算法在F测量和G均值方面优于一些最先进的算法。

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