Abstract
Data mining is the procedure utilized for segregating the cardinal data from humongous amounts of data or data warehouses. The data comprises outliers which are deviations from the regular data. The outlier identification is a significant preprocessing strategy to discard the data that strays away from ordinary information. The anomaly recognition which is present in the huge database, is broadly utilized in different applications like medical diagnosis, human reliability analysis in health care, flaw identification. The primary target of the outlier identification is to extricate the strange information that has abnormal characteristics. The data gathering from the various systems is ambiguous in nature. The data ambiguity leads to irregularity and vagueness. This paper proposes a novel Hybrid Fuzzy Rough Set Classifier (HFRSC) for an effectual classification with less, analytical and computing effort.
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Meda, S., Bhogapathi, R.B. (2021). A Hybrid Framework for Prediction of Heart Disease Using Rough Set and Fuzzy Set Approach. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_50
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DOI: https://doi.org/10.1007/978-981-16-1941-0_50
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