A Novel Online Real-time Classifier for Multi-label Data Streams

TL;DR

This paper introduces a new online multi-label classifier based on extreme learning machines that can process data streams in real-time, outperforming existing methods in speed and accuracy.

Contribution

It presents the first real-time neural network-based multi-label classifier for data streams using extreme learning machines, addressing a gap in current research.

Findings

Outperforms state-of-the-art techniques in speed and accuracy

Successfully classifies multi-label data streams in real-time

Validated on datasets from various application domains

Abstract

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.