Literature review and analysis on big data stream classification techniques
| Autor: | B. Padmaja Rani, N. Sandhya, B. Srivani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial Intelligence
Control and Systems Engineering business.industry Computer science 020204 information systems Big data 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology business Data science Software |
| Zdroj: | International Journal of Knowledge-based and Intelligent Engineering Systems. 24:205-215 |
| ISSN: | 1875-8827 1327-2314 |
| DOI: | 10.3233/kes-200042 |
| Popis: | Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented. |
| Databáze: | OpenAIRE |
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