A survey on multi label data stream classification

1. 2020 – 2021
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A Survey on Multi-Label Data Stream Classification
Abstract
Nowadays, many real-world applications of our daily life generate massive volume of
streaming data at a higher speed than ever before, to name a few, Web clicking data
streams, sensor network data and credit transaction streams. Contrary to traditional
data mining using static datasets, there are several challenges for data stream mining,
for instance, finite memory, one-pass and timely reaction. In this survey, we provide a
comprehensive review of existing multi-label streams mining algorithms and categorize
these methods based on different perspectives, which mainly focus on the multi-label
data stream classification. We first briefly summarize existing multi-label and data
stream classification algorithms and discuss their merits and demerits. Secondly, we
identify mining constraints on classification for multi-label streaming data, and present a
comprehensive study in algorithms for multi-label data stream classification. Finally,
several challenges and open issues in multi-label data stream classification are
discussed, which are worthwhile to be pursued by the researchers in the future.