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• W3129095246 abstract "The tremendous growth in the technology has led to the accumulation of enormous Big Data. Techniques that efficiently analyse this Big Data are in great demand. Tweets from Social media and Sensor data are some of the most common forms of Big Data. Machine learning algorithms pave way for researchers to analyze Big Data. Most Machine learning algorithms depend on efficient feature extraction and feature selection for its success. Here, we explore feature selection methods like entropy and Rough set on the sensor data. Also a symbolic approach of feature extraction is proposed which represents both sensor and twitter data efficiently for further data analysis. Some popular classifiers like Naïve Bayes, K Nearest Neighbour, Support Vector Machine and Decision Tree are used for validating the efficacy of the features selected. An ensemble classifier technique is also proposed which is compared with various state of the art ensemble classifiers. Symbolic features perform better than both entropy and Rough set features for sensor data and improves the clustering efficiency of twitter data. The proposed ensemble weighted average classifier on Symbolic features outperform all the other ensemble classifiers and independent classifiers. The results obtained from these methods have the potential to aid the public health surveillance." @default.
• W3129095246 created "2021-02-15" @default.
• W3129095246 creator A5018541093 @default.
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• W3129095246 date "2021-07-01" @default.
• W3129095246 modified "2023-09-27" @default.
• W3129095246 title "Effective feature representation using symbolic approach for classification and clustering of big data" @default.
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• W3129095246 doi "https://doi.org/10.1016/j.eswa.2021.114658" @default.
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