Concept Evolution Detection Method for Open Feature Space

SU Rui; GUO Hu-sheng; WANG Jing; WANG Wen-jian

doi:10.12263/DZXB.20250416

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Concept Evolution Detection Method for Open Feature Space

PAPERS | 更新时间：2026-02-05

- Concept Evolution Detection Method for Open Feature Space
- ACTA ELECTRONICA SINICA Vol. 53, Issue 10, Pages: 3718-3729(2025)
- 作者机构：
  
  1.山西大学计算机与信息技术学院，山西太原 030006
  2.山西大学计算智能与中文信息处理教育部重点实验室，山西太原 030006
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U21A20513;62276157;62476157);Natural Science Foundation of Shanxi Province(202202020101003)
- DOI：10.12263/DZXB.20250416
  CLC： TP391;
- Received：25 May 2025，
  
  Accepted：11 October 2025，
  
  Published：25 October 2025
- 稿件说明：
移动端阅览
苏睿, 郭虎升, 王婧, 等. 面向开放特征空间的概念演化检测方法[J]. 电子学报, 2025, 53(10): 3718-3729.

SU Rui, GUO Hu-sheng, WANG Jing, et al. Concept Evolution Detection Method for Open Feature Space[J]. Acta Electronica Sinica, 2025, 53(10): 3718-3729.
苏睿, 郭虎升, 王婧, 等. 面向开放特征空间的概念演化检测方法[J]. 电子学报, 2025, 53(10): 3718-3729. DOI：10.12263/DZXB.20250416

SU Rui, GUO Hu-sheng, WANG Jing, et al. Concept Evolution Detection Method for Open Feature Space[J]. Acta Electronica Sinica, 2025, 53(10): 3718-3729. DOI：10.12263/DZXB.20250416

摘要

在众多现实场景中数据以流的形式持续产生，由于流数据具有动态变化的特点，在生成过程中可能产生新的类别，也被称为概念演化.概念演化是流数据挖掘模型预测性能衰退甚至预测失效的主要原因.因此，能及时发现类空间变化并提醒模型做出适应性调节的概念演化检测方法受到广泛关注.然而，目前多数概念演化检测方法基于特征空间静态不变的假设构建算法.在现实场景中，特征空间同样具有动态性，属于开放的空间.具体来说，随时间推移可能出现部分特征消失和新特征产生的现象，从而破坏上述假设并导致已有算法失效.针对这一问题，本文提出一种面向开放特征空间的概念演化检测方法（Concept evolution Detection method for Open Feature space，CD_OF）.该方法通过构建微簇集成模型对新进实例分类，对于开放特征空间中的旧特征消失问题，通过利用转移矩阵将旧特征中包含的信息转换到共享特征中；对于新出现的特征，拓展共享特征空间并重构集成模型.在此基础上，根据样本的共享邻域信息定义样本间相似度以检测概念演化，并建立动态衰减模型，以解决开放特征空间下的类消失和类循环问题.实验结果表明，本文所提出的方法能够对开放特征空间中特征的变化作出及时响应，增强概念演化检测的能力，在特征空间变化的真实流数据中与现有方法相比，错误率降低了1.7%~11.4%.

Abstract

In many real-world scenarios

data is continuously generated in the form of streams. Due to the dynamic nature of streaming data

new categories may emerge during the generation process

which is known as concept evolution. Concept evolution is one of the primary challenges leading to the degradation or even failure of predictive performance in stream mining models. Therefore

concept evolution detection methods capable of promptly identifying changes in the class space and alerting models to perform adaptive adjustments have attracted widespread attention. However

most of the current concept evolution detection methods construct algorithms based on the assumption that the feature space is static and unchanging. In real scenarios

the feature space is also dynamic and belongs to the open space. Specifically

over time

some features may disappear and new features may emerge

thus violating the above assumption and causing existing algorithms to fail. To address this problem

this paper proposes a concept evolution detection method for open feature space (CD_OF). The method constructs a micro-cluster ensemble model to classify incoming instances. For the problem of disappearing old features in the open feature space

the information contained in the old features is converted to the shared features through the transfer matrix; for the newly emerged features

the shared feature space is expanded and the integration model is reconstructed. On this basis

the inter-sample similarity is defined based on the shared neighborhood information of the samples to detect concept evolution

and the dynamic decay model is established to solve the class vanishing and classifications cycling problems under the open feature space. The experimental results show that the method proposed in this paper is able to respond to the changes of features in the open feature space in a timely manner and enhance the ability of concept evolution detection. The error rate is reduced by 1.7% to 11.4% compared to existing methods on real streaming data with feature space variations.

关键词

Keywords

references

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