Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

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


Affiliations
1 Sushant University, Gurugram, India
2 GIS Cell, MNNIT Prayagraj, Allahabad, India
3 ABES Engineering College, Ghaziabad, Uttar Pradesh, India
4 Maharaja Surajmal Institute of Technology, New Delhi, India
5 G B Pant Engineering College, New Delhi, India
6 Amity University, Noida, India
7 Sant Baba Attar Singh Khalsa College, Sandaur, Punjab, India

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.
User
Notifications
Font Size

Abstract Views: 119




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

Abstract Views: 119  | 

Authors

Garima Bakshi
Sushant University, Gurugram, India
Rati Shukla
GIS Cell, MNNIT Prayagraj, Allahabad, India
Vikash Yadav
ABES Engineering College, Ghaziabad, Uttar Pradesh, India
Aman Dahiya
Maharaja Surajmal Institute of Technology, New Delhi, India
Rohit Anand
G B Pant Engineering College, New Delhi, India
Nidhi Sindhwani
Amity University, Noida, India
Harinder Singh
Sant Baba Attar Singh Khalsa College, Sandaur, Punjab, 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.