Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

doi:10.1109/TEVC.2022.3232466

Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

2023-08-01

8

OA

AI

焦博韬 (Botao Jiao)

郭一楠 (Yinan Guo) *

杨

杨圣祥 (Shengxiang Yang)

DOI：10.1109/TEVC.2022.3232466

Original

Original request for help

Save

Abstract

En 中文

In traditional data stream mining, classification models are typically trained on labeled samples from a single source. However, in real-world scenarios, obtaining accurate labels is very hard and expensive, especially, when multiple data streams are concurrently sampled from an environment or the same process. To address this issue, multistream classification is proposed, in which a data stream with biased labels (called the source stream) is leveraged to train a suitable model for prediction over another stream with unlabeled samples (called the target stream). Despite the growing research in this field, previous multistream classification methods are mostly designed for single-source stream scenarios. However, various source streams contain diverse data distributions, providing more valuable information for building a more accurate model. In addition, previous works construct classification models in the original shared feature space, ignoring the effect of redundant or low-quality features on the classification performance. This may produce inefficient knowledge transfer across streams. In view of this, a reduced-space multistream classification based on multiobjective evolutionary optimization is proposed in this article. First, a multiobjective evolutionary optimization is employed to seek the most valuable feature subset shared in the source and target domains, with the purpose of narrowing the distribution difference between source and target streams. Following that, a Gaussian mixture model-based weighting mechanism for source samples is presented. More especially, two drift adaptation methods are proposed to address asynchronous drift. Experimental results on benchmark datasets show that the proposed method outperforms other comparative methods on classification accuracy and G-mean.

Keywords:

classification accuracy and G-mean. Index Terms-Concept drift

domain adaption

feature selec-tion

multiobjective optimization

multistream classification

Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

Abstract

Journal

Organization