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Ordinal pattern-based graph classification method

A pattern and classifier technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as ignoring multiple node information, affecting classification results, and affecting classification performance

Inactive Publication Date: 2016-12-07
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, thresholding will lose a lot of weight information in the loss map, which will affect the final classification results
Second, most graph features (node ​​degree, clustering coefficient, etc.) only consider the information of a single node, while ignoring the information between multiple nodes
Obviously, these two shortcomings will greatly affect the final classification performance

Method used

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  • Ordinal pattern-based graph classification method
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  • Ordinal pattern-based graph classification method

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Embodiment

[0035] Such as figure 2 As shown, the specific implementation process includes four steps:

[0036] The first step is to mine frequent ordered patterns. In the process of mining frequent ordered patterns, a depth-first search tree is constructed to search all ordered patterns and judge whether they meet the frequency condition. In the search process, the Apriori property of the ordered pattern is used, that is, the frequency of an ordered pattern is not lower than that of any ordered pattern derived from it. exist image 3 An example diagram of the search process is given in . In the graph, each point represents an edge, and all edges from the root node to the current point constitute the current ordered pattern. Then, the frequency of the current ordered pattern is calculated. If the frequency is higher than a predefined threshold, the current ordered pattern is a frequent ordered pattern (eg op i ), continue to search whether the ordered pattern derived from it is a f...

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Abstract

The invention discloses a graph classification method. A graph as a commonly used dataset structure can be used for expressing various complex relations among data objects in many scientific applications. According to the method disclosed in the invention, a new graph characteristic which is an ordinal pattern is put forward, and weight information and a local topology structure in the graph can be retained via the ordinal pattern which is therefore an ideal graph character. Via a series of algorithms, a distinguishing ordinal pattern is extracted from the graph and is used as a character. Finally, based on a support vector machine, a classifier used for graph classification is built. Via the method disclosed in the invention, efficient and accurate graph classification can be realized.

Description

technical field [0001] The invention discloses a graph classification method based on an ordered pattern, which involves neural image processing, social network, frequent item mining, classifier construction, etc., and aims to realize accurate and efficient classification of graph data. Background technique [0002] As a general data set structure, graph can be used to represent various complex relationships among data objects in many scientific applications. For example, construct a graph based on neuroimaging, and then analyze and study the graph through technologies such as complex networks, or use the graph structure to represent the structure of compounds. At present, the graph classification problem mainly studies the two classification problems, that is, the positive class and the negative class. The main goal is to build a classification model to separate the two. In recent years, many kinds of graph features have been used for graph classification. For example, no...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 张道强屠黎阳杜俊强
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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