Multi-label classification via incremental clustering on an evolving data stream

Tien Thanh Nguyen

doi:10.1016/j.patcog.2019.06.001

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Multi-label classification via incremental clustering on an evolving data stream

2019-11-01

Tien Thanh Nguyen

Truong Dang

Anh Vu Luong

Alan Wee‐Chung Liew *

Tiancai Liang

John McCall

DOI：10.1016/j.patcog.2019.06.001

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Abstract

En 中文

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings. (C) 2019 Elsevier Ltd. All rights reserved.

Keywords:

Multi-label classification

Incremental learning

Online learning

Clustering

Data stream

Concept drift

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