Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55728
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dc.contributor.authorYadav, Vikash-
dc.contributor.authorRahul, Mayur-
dc.contributor.authorShukla, Rati-
dc.date.accessioned2020-12-01T09:47:18Z-
dc.date.available2020-12-01T09:47:18Z-
dc.date.issued2020-12-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55728-
dc.description1095-1100en_US
dc.description.abstractMulti-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.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.79(12) [December 2020]en_US
dc.subjectFeature Selectionen_US
dc.subjectInductive Logical Programmingen_US
dc.subjectNatural Joinen_US
dc.subjectSVMen_US
dc.subjectStatistical Learningen_US
dc.titleA New Improved Approach for Feature Generation and Selection in Multi-Relational Statistical Modelling using MLen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.79(12) [December 2020]

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