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A novel classifier for multivariate instance using graph class signatures

Frontiers of Computer Science 2020年第4期14卷 79-94页

作者： parnika paranjape Meera DHABU Parag DESHPANDE Department of Computer Science Visvesvaraya National Institute of TechnologyNagpurMaharashtra440010India

Applications like identifying different customers from their unique buying behaviours,determining ratings of a product given by users based on different sets of features,*** classification using class-specific subsets of *** of the existing state-of-the-art classifiers for multivariate data use complete feature set for classification regardless of the different class *** tree classifier can produce class-wise subsets of ***,none of these classifiers model the relationship between features which may enhance classification *** call the class-specific subsets of features and the features’interrelationships as class *** this work,we propose to map the original input space of multivariate data to the feature space characterized by connected graphs as graphs can easily model entities,their attributes,and relationships among ***,entities are modeled using graphs,where graphs occur naturally,for example,chemical ***,graphs do not occur naturally in multivariate ***,extracting class signatures from multivariate data is a challenging *** propose some feature selection heuristics to obtain class-specific prominent subgraph *** also propose two variants of class signatures based classifier namely:1)maximum matching signature(gMM),and 2)score and size of matched signatures(gSM).The effectiveness of the proposed approach on real-world and synthetic datasets has been studied and compared with other established *** results confirm the ascendancy of the proposed class signatures based classifier on most of the datasets.

关键词： multivariate instance data transformation subgraph feature selection class signatures classification

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