| 摘要: |
| 目的 特征演化数据流的特征空间随时间推移而动态变化,传统增量学习方法囿于固定特征空间的假设,无
法直接应用于特征演化数据流的学习场景,因此针对挖掘特征演化数据流时面对的分类模型与当前数据特征不匹
配而失效、模型预测性能受噪声干扰等问题,提出了一种面向特征演化数据流的增量学习方法。 方法 首先,通过引
入模糊隶度函数并结合增量孪生支持向量机模型,鲁棒地训练与更新分类器;当出现新特征时,重新训练新分类
器,同时结合局部线性加权回归算法拟合新旧特征之间的映射关系,从而在旧特征消失时,利用所学到的映射关
系,将已训练好的旧分类器投影至新特征空间继续更新;最后,结合两种不同的集成策略以合并新旧两分类器实现
共同预测。 结果 通过大量仿真实验,所提方法分类准确率相较于对比方法提升了 0. 3% ~ 21. 7%;在含不同信噪比
数据集上,分类模型性能整体优于对比模型,并随着人工增加噪声比例,模型分类效果受负面影响较小。 结论 所提
方法得以构建性能高效稳定的分类模型,在提升模型预测精度的同时能减少噪声对分类性能的干扰,增强了模型
对特征演化数据流自适应学习能力。 |
| 关键词: 数据流挖掘 特征演化 增量学习 动态数据流 集成学习 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Incremental Learning Method for Feature-evolving Data Streams |
|
CHEN Yanfei LIU Sanmin
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School of Computer and Information Anhui Polytechnic University Anhui Wuhu 241000 China
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| Abstract: |
| Objective The feature space of feature-evolving data streams dynamically changes over time. Traditional
incremental learning methods are constrained by the assumption of a fixed feature space and cannot be directly applied to
the learning scenario of feature-evolving data streams. Therefore to address the problems of classification models
mismatching with current data features and model prediction performance being affected by noise interference when mining
feature-evolving data streams an incremental learning method tailored to feature-evolving data streams was proposed.
Methods Firstly by introducing fuzzy membership functions and combining them with an incremental twin support vector
machine model classifiers were robustly trained and updated. When new features appeared new classifiers were
retrained and the mapping relationship between new and old features was fitted using a local linear weighted regression
algorithm. Thus when old features disappeared the trained old classifiers were projected into the new feature space for
continued updating using the learned mapping relationship. Finally two different ensemble strategies were combined to merge the new and old classifiers for joint prediction. Results Through extensive simulation experiments the proposed
method improved classification accuracy by 0. 3% to 21. 7% compared with baseline methods. On datasets with different
signal-to-noise ratios the overall performance of the classification model was superior to that of the baseline model and
the model ?? s classification effectiveness was less affected by artificially increasing the noise ratio. Conclusion The
proposed method is verified to construct an efficient and stable classification model which not only enhances model
prediction accuracy but also reduces the interference of noise on classification performance thus strengthening the
adaptive learning capability of the model for feature-evolving data streams. |
| Key words: data stream mining feature evolution incremental learning dynamic data streams ensemble learning |
