Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57918
Title: An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning
Authors: Bakshi, Garima
Shukla, Rati
Yadav, Vikash
Dahiya, Aman
Anand, Rohit
Sindhwani, Nidhi
Singh, Harinder
Keywords: Classification-type approach;Data mining;Feature space;Statistical model;Support Vector Machine
Issue Date: Jun-2021
Publisher: NIScPR-CSIR, India
Abstract: Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results.
Page(s): 537-542
URI: http://nopr.niscair.res.in/handle/123456789/57918
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(06) [June 2021]

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