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An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

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Title An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning
 
Creator Bakshi, Garima
Shukla, Rati
Yadav, Vikash
Dahiya, Aman
Anand, Rohit
Sindhwani, Nidhi
Singh, Harinder
 
Subject Classification-type approach
Data mining
Feature space
Statistical model
Support Vector Machine
 
Description 537-542
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.
 
Date 2021-08-23T10:17:10Z
2021-08-23T10:17:10Z
2021-06
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/57918
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(06) [June 2021]