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A new improved Approach for Feature Generation and Selection in multirelationalstatistical modelling using machine learning

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Title Statement A new improved Approach for Feature Generation and Selection in multirelationalstatistical modelling using machine learning
 
Added Entry - Uncontrolled Name Yadav, Vikash ; ABES Engineering College, Ghaziabad
Rahul, Mayur ; CSJM UIET kanpur
Shukla, Rati ; MNNIT Allahabad
 
Uncontrolled Index Term Support Vector Machine; Natural Join; Statistical Learning; Multi-relation; Inductive Logical Programming; Clusters; Feature Selection
 
Summary, etc. Multi-relational classification is highly challengeable task in data mining, becauseso much data in our world is organised in multiple relations. The challenge comes from thehuge collection of search spaces and high calculation cost arises in the selection of featuredue to excessive complexity in the various relations. The state-of-the-art approach is based onclusters and inductive logical programming to retrieve important features and derivedhypothesis. However, those techniques are very slow and unable to create enough data andinformation to produce efficient classifiers. In the given paper, we proposed a fast andeffective method for the feature selection using multi-relational classification. Moreover weintroduced the natural join and SVM based feature selection in multi-relation statisticallearning. The performance of our model on various datasets indicates that our model isefficient, reliable and highly accurate.
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-01-11 11:19:19
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/38116
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 12 (20)
 
Language Note en