Abstract
Online Feature Selection (OFS) is an important technique in pattern recognition and machine learning. Our challenge is how to enhance the classification performance in real contexts where the large-scale training data arrive sequentially with a big number of features. The major problem is how to choose the best accurate and efficient state-of-the art OFS method that can select the relevant features or if we do a combination between these methods can we improve the classification performance? In this paper, we propose a framework of OFS using the characteristics of multi-agent systems (MAS) to overcome this challenge. We propose firstly a new OFS model; Agent-Learner based OFS (ALOFS) which represents each agent in our MAS. ALOFS is a generalization of first-order and second-order online learning methods based feature selection. Secondly, we propose the Multi Agent-Learner based OFS (MALOFS) system which is our MAS. MALOFS uses two levels of selection: the first level aims to select the more confident learners and the second level has as object to select the relevant features using a proposed negotiation method (MANOFS). MALOFS is applicable to different domains successfully and achieves highly accuracy with some real world applications.
Published Version
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