A novel online multi-label classifier for high-speed streaming data applications

TL;DR

This paper introduces a high-speed online neural network classifier based on extreme learning machines for real-time multi-label streaming data classification, demonstrating superior performance and speed over existing methods.

Contribution

The paper presents a novel threshold-based online sequential learning algorithm leveraging extreme learning machines for efficient multi-label classification of streaming data.

Findings

Outperforms nine state-of-the-art methods in accuracy and speed

Effective across diverse application domains such as multimedia, text, and biology

Achieves real-time classification with low training and testing times

Abstract

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label classification. Multi-label classification problems are far more complex than binary and multi-class classification problems, as both the number of target labels and each of the target labels corresponding to each of the input samples are to be identified. The proposed work exploits the high-speed nature of the extreme learning machines to achieve real-time multi-label classification of streaming data. A new threshold-based online sequential learning algorithm is proposed for high speed and streaming data classification of multi-label problems. The proposed method is experimented with six different datasets from different application domains such as multimedia, text, and biology. The hamming loss, accuracy, training time and testing time of the proposed technique is compared with nine different state-of-the-art methods. Experimental studies shows that the proposed technique outperforms the existing multi-label classifiers in terms of performance and speed.