Online feature selection system for big data classification based on multi-objective automated negotiation

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. (c) 2020 Elsevier Ltd. All rights reserved.