Ensemble learning for network data stream classification using similarity and online genetic algorithm classifiers

Raja, M. Arun Manicka; Swamynathan, S.

doi:10.1109/icacci.2016.7732277

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…In their work in a similar domain, He et al experimented with social question-and-answer sites corpora on two disease domains -diabetes and cancer-in order to identify new, meaningful consumer terms [42]. Others developed a model comprising an ensemble of classifiers for mining social media data streams by combining similarity-based and genetic algorithm classifiers [43].…”

Section: Similarity-based Approachesmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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Section: Similarity-based Approachesmentioning

confidence: 99%