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Feature selection on probabilistic symbolic objects

Frontiers of Computer Science 2014年第6期8卷 933-947页

作者： Djamal ZIANI Information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.

关键词： symbolic data analysis feature selection probabilistic symbolic object discrimination criteria data and knowledge visualization.

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Hierarchical Clustering of Complex symbolic data and Application for Emitter Identification

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Journal of Computer Science & Technology 2018年第4期33卷 807-822页

作者： Xin Xu Jiaheng Lu Wei Wang Laboratory of Science and Technology on Information System Engineering Nanjing Research Institute of Electronics Engineering Nanjing 21000~ China Department of Computer Science University of Helsinki Helsinki 00014 Finland State Key Laboratory for Novel Software and Technology Nanjing University Nanjing 210046 China

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.

关键词： symbolic data analysis stochastic pattern fuzzy interval hierarchical clustering emitter identification

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Features Extraction in the Copper Futures Market of China Based on DIV Clustering Method of Interval data

Features Extraction in the Copper Futures Market of China Ba...

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第二十九届中国控制会议

作者： MENG Jie School of Statistics,Central University of Finance and Economics,Beijing 100081,P.R.China

In futures market,daily trading records of numerous contracts compose a large scale *** to integrate the mass data,extract the general features,and find out the underlying operational regularity of the market are significant to instruct the sound development of the *** that research motivation,this paper utilizes the symbolic data analysis（SDA）methodology to model on the large scale database of the copper futures contracts in Shanghai Futures Exchange（SHFE）.First,classify the mass and complex time-series trading records in the temporal dimension as their residual time to maturity,so that the original data set of single-values is transformed into the high-level structure of symbolic interval data which greatly reduces the dimension scale of the sample *** on that,symbolic DIV clustering method is applied to the interval data and three clusters partitioned by the size of trading volume are ***,ZoomStar graphs of the three clusters are also clearly illustrated the variation features of the copper *** results of the empirical study indicate that in the whole valid period of transaction,speculations present a "less-more-less" quantity in trading volume and open interest,and the tradings in two and three months to maturityare especiallymore active than in the other *** application research also verifies the validityand practicabilityof SDA in integrating complex system,effectively modeling and information mining.

关键词： symbolic data analysis Interval data DIV Clustering Futures Market

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