A Novel Online Real-time Classifier for Multi-label Data Streams
This addresses the need for efficient multi-label classification in real-time applications, though it is incremental as it adapts an existing method to a new problem.
The authors tackled the lack of real-time online neural network classifiers for multi-label data streams by developing an extreme learning machine-based method, which outperformed state-of-the-art techniques in speed and accuracy.
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In contrast to traditional binary and multi-class classification, multi-label classification involves association of each of the input samples with a set of target labels simultaneously. There are no real-time online neural network based multi-label classifier available in the literature. In this paper, we exploit the inherent nature of high speed exhibited by the extreme learning machines to develop a novel online real-time classifier for multi-label data streams. The developed classifier is experimented with datasets from different application domains for consistency, performance and speed. The experimental studies show that the proposed method outperforms the existing state-of-the-art techniques in terms of speed and accuracy and can classify multi-label data streams in real-time.