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A New Improved Approach for Feature Generation and Selection in Multi-Relational Statistical Modelling using ML

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Title A New Improved Approach for Feature Generation and Selection in Multi-Relational Statistical Modelling using ML
 
Creator Yadav, Vikash
Rahul, Mayur
Shukla, Rati
 
Subject Feature Selection
Inductive Logical Programming
Natural Join
SVM
Statistical Learning
 
Description 1095-1100
Multi-relational classification is highly challengeable task in data mining, because so much data in our world is organised in multiple relations. The challenge comes from the huge collection of search spaces and high calculation cost arises in the selection of feature due to excessive complexity in the various relations. The state-of-the-art approach is based on clusters and inductive logical programming to retrieve important features and derived hypothesis. However, those techniques are very slow and unable to create enough data and information to produce efficient classifiers. In the given paper, we proposed a fast and effective method for the feature selection using multi-relational classification. Moreover we introduced the natural join and SVM based feature selection in multi-relation statistical learning. The performance of our model on various datasets indicates that our model is efficient, reliable and highly accurate.
 
Date 2020-12-01T09:47:18Z
2020-12-01T09:47:18Z
2020-12
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/55728
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source JSIR Vol.79(12) [December 2020]