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dc.contributor.authorKumar, Anil
dc.contributor.authorKabra, Gaurav
dc.contributor.authorMussada, Eswara Krishna
dc.contributor.authorDash, Manoj Kumar
dc.contributor.authorRana, Prashant Singh
dc.date.accessioned2019-01-17T17:41:24Z
dc.date.available2019-01-17T17:41:24Z
dc.date.issued2017-05-30
dc.identifier.citationKumar, A. et al (2017) ‘Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention’, Neural Computing and Applications. Doi: 10.1007/s00521-017-3047-zen
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.doi10.1007/s00521-017-3047-z
dc.identifier.urihttp://hdl.handle.net/10545/623312
dc.description.abstractA novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/10.1007/s00521-017-3047-zen
dc.rightsArchived with thanks to Neural Computing and Applicationsen
dc.subjectArtificial bee colony algorithmen
dc.subjectclassificationen
dc.subjectconsumeren
dc.subjectK-fold cross validationen
dc.subjectprediction sensitivityen
dc.titleCombined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intentionen
dc.typeArticleen
dc.contributor.departmentXavier Institute of Management, Bhubaneswar, Indiaen
dc.contributor.departmentBML Munjal Universityen
dc.contributor.departmentIndian Institute of Information Technology & Management, Gwalior (Madhya Pradesh), Indiaen
dc.contributor.departmentThapar University Patiala, Punjab, Indiaen
dc.identifier.journalNeural Computing and Applicationsen
dc.dateAccepted2017-05-23
dc.dateAccepted2017-05-23
dc.dateAccepted2017-05-23
dc.dateAccepted2017-05-23
dc.dateAccepted2017-05-23
refterms.dateFOA2018-05-30T00:00:00Z
html.description.abstractA novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.


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