Abstract Feature Selection (FS) plays an important role in learning and classification tasks. Its objective is to select the relevant and non-redundant features. Considering the huge number of features in real-world applications, FS methods using batch learning technique cannot resolve big data problems especially when data arrive sequentially. In this paper, we proposed an online feature selection system which resolves this problem. The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation based Online Feature Selection) explore the recent advances of online machine learning techniques and a conflict resolution technique (Automated Negotiation) for the purpose of enhancing the classification performance of ultra-high dimensional databases. MOANOFS uses two decision levels. In the first level, we decided which k(s) among the learners (or OFS methods) are the trustful ones (with high confidence or trust value). These elected k learners would participate in the second level where we integrated our proposed Multilateral Automated Negotiation based OFS (MANOFS) method. This would enable us to finally decide which features are the most relevant. We showed that MOANOFS system achieves high accuracy with several real text classification datasets as 20Newsgroups, RCV1.

中文翻译：

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基于多目标自动协商的大数据分类在线特征选择系统

摘要 特征选择（FS）在学习和分类任务中起着重要作用。它的目标是选择相关的和非冗余的特征。考虑到实际应用中的大量特征，使用批量学习技术的 FS 方法无法解决大数据问题，尤其是当数据按顺序到达时。在本文中，我们提出了一个在线特征选择系统来解决这个问题。提议的 OFS 系统称为 MOANOFS（基于在线特征选择的多目标自动协商）探索了在线机器学习技术和冲突解决技术（自动协商）的最新进展，目的是提高超高维数据库的分类性能。MOANOFS 使用两个决策级别。在第一级，我们决定了学习器（或 OFS 方法）中哪些 k(s) 是可信任的（具有高置信度或信任值）。这些选出的 k 个学习者将参与第二级，在那里我们集成了我们提出的基于多边自动协商的 OFS（MANOFS）方法。这将使我们能够最终决定哪些功能最相关。我们展示了 MOANOFS 系统在多个真实文本分类数据集（如 20Newsgroups、RCV1.0）上实现了高精度。