dSubSign: Classification of Instance-Feature Data Using Discriminative Subgraphs as Class Signatures
| Autor: | Parnika Paranjape, Parag S. Deshpande, Meera Dhabu |
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| Rok vydání: | 2021 |
| Předmět: |
Feature data
Computer Networks and Communications Computer science business.industry Pattern recognition Feature selection 02 engineering and technology Customer identification Missing data Computer Graphics and Computer-Aided Design Class (biology) ComputingMethodologies_PATTERNRECOGNITION Discriminative model Artificial Intelligence Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 31:917-947 |
| ISSN: | 1793-6403 0218-1940 |
| Popis: | Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods. |
| Databáze: | OpenAIRE |
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