Role of big-data in classification and novel class detection in data streams

Chandak, M. B.

doi:10.1186/s40537-016-0040-9

Cited by 26 publications

(9 citation statements)

References 10 publications

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“…The user engagement rates, responses, comments, shares, likes and profile were randomly sampled from different hubs like Twitter, Facebook, Reddit, Gab, and public blogs. The hashtags, 1,4,5,16–20 COVIDINDIA and CORONAVIRUS were the targeted context to trace tweets associated with COVID‐19 symptoms. The geographical features like location of the user within the sort of latitude‐longitude and therefore the time of social involvement is captured.…”

Section: Data Stream Collection For Pretrainingmentioning

confidence: 99%

RMCL: A deep learning based recursive malicious context learner in social networks

Tiwari

2022

Computational Intelligence

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Propagation of malicious content and its promotion is a recurrent problem prominent in social networks. Detection and interactive labeling of malicious context promotion on-the-fly is a very challenging and difficult task due to the lack of complete knowledge about the underlying context. Modern research in this field use neural networks and text encoders to analyze the information dynamically which is inefficient, in terms of time-space consumed in the overall process. This article proposes an online active learning system to label the data streams on-the-fly by sampling them in the form of tweet-retweet-follow (trf) sequences in social networks. A heuristically pretrained recursive malicious context learner is fixed for knowledge acquisition, data accommodation and pseudonymization in the form of a transformer in the tree structured recursive neural network. The data stream is trained using recursive bidirectional training to capture long-term dependencies. The COVID-19 data streams are selectively sampled on-the-fly using psycho linguistic words in the proposed experiment and are labeled deceptive/nondeceptive based on knowledge learned during pretraining. The proposed recursive malicious context learner successfully resolves the problem of 1956

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Section: Data Stream Collection For Pretrainingmentioning

confidence: 99%

RMCL: A deep learning based recursive malicious context learner in social networks

Tiwari

2022

Computational Intelligence

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“…Various researchers mainly solved the issues of infinite size and perception development, but the perception flow and characteristic assessment are not taken into consideration by researchers. Here, these two issues are removed in implementing a classification method over big data and parameters like error rate and running time are obtained to provide superior efficiency of the method [34].…”

Section: Literature Surveymentioning

confidence: 99%

CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

Ahirwar

Shukla

Singhai³

2021

Scientific Programming

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Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.

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“…Chandak [37] proposed a string-based data stream processing method, which mainly solves the problem of data stream concept evolution through the CON_EVOLUTION algorithm. Miao et al [38] solved the problem that only the numerical data can be solved in the framework of MineClass algorithm.…”

Section: Novel Class Detection In the Presence Of Concept Driftmentioning

confidence: 99%

A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

Zhou

Jin

et al. 2020

Journal of Electrical and Computer Engineering

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Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept evolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this paper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis distance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and novel class detection.

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Role of big-data in classification and novel class detection in data streams

Cited by 26 publications

References 10 publications

RMCL: A deep learning based recursive malicious context learner in social networks

RMCL: A deep learning based recursive malicious context learner in social networks

CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

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