An unsupervised feature extraction and fusion framework for multi-source data based on copula theory

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiuwei Chen, Li Lai, Maokang Luo
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引用次数: 0

Abstract

With the development of big data technology, people are increasingly facing the challenge of dealing with massive amounts of multi-source or multi-sensor data. Therefore, it becomes crucial to extract valuable information from such data. Information fusion techniques provide effective solutions for handling multi-source data and can be categorized into three levels: data-level fusion, feature-level fusion, and decision-level fusion. Feature-level fusion combines features from multiple sources to create a consolidated feature, enhancing information richness. This paper proposes an unsupervised feature extraction and fusion method for multi-source data that utilizes the R-Vine copula, denoted as CF. The method starts by performing kernel density estimation to extract each data source's marginal density and distribution. Next, the maximum spanning tree is employed to select a vine structure for each attribute, and the corresponding copulas are chosen using maximum likelihood estimation and the AIC criterion. The joint probability density of each attribute across all information sources can be obtained by utilizing the relevant vine structure and copulas, serving as the final fusion feature. Finally, the proposed method is evaluated on eighteen simulated datasets and six real datasets. The results indicate that compared to several state-of-the-art fusion methods, the CF method can significantly enhance the classification accuracy of popular classifiers such as KNN, SVM, and Logistic Regression.
基于copula理论的多源数据无监督特征提取与融合框架
随着大数据技术的发展,人们越来越多地面临着处理海量多源或多传感器数据的挑战。因此,从这些数据中提取有价值的信息变得至关重要。信息融合技术为处理多源数据提供了有效的解决方案,可分为数据级融合、特征级融合和决策级融合三个层次。特征级融合将来自多个来源的特征组合在一起,创建一个统一的特征,增强了信息的丰富性。本文提出了一种基于R-Vine copula的多源数据无监督特征提取与融合方法,该方法首先进行核密度估计,提取每个数据源的边缘密度和分布。其次,使用最大生成树为每个属性选择一个藤结构,并使用最大似然估计和AIC准则选择相应的copula。利用相关的藤状结构和联结得到各属性在所有信息源上的联合概率密度,作为最终的融合特征。最后,在18个模拟数据集和6个真实数据集上对该方法进行了评估。结果表明,与几种最先进的融合方法相比,CF方法可以显著提高KNN、SVM和Logistic回归等常用分类器的分类精度。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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