Abstract
Human activities can be recognized by application of several sensors on different body parts. This leads to generation of big data which is also imbalance in nature. Classification of such imbalanced big data is a tedious task because performance of traditional machine learning algorithms becomes limited in this scenario. To deal with imbalanced classification problem for human activity recognition, fuzzy logic along with MapReduce architecture to handle big data has been used in this paper. Fuzzy rule-based classification system techniques FRBCS.CHI and FRBCS.W have been used for learning in imbalanced big data. Results show that fuzzy algorithms have performed well in prediction as imbalance ratio of dataset is increased.
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Ahlawat, K., Singh, A.P. (2020). Human Activity Recognition in Imbalanced Big Data Using Fuzzy Rule-Based Classification System. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_4
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DOI: https://doi.org/10.1007/978-981-15-2475-2_4
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