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- W4285260944 abstract "Class imbalance is not new in the world of Machine Learning (ML). In this digital world, almost all physical systems are converted to automated systems. Digitization of systems generates a huge amount of data. Domains like healthcare, finance, security drastically improve their performance and business too. Digital data is categorized according to its domains and it is popular as data set. For effective utilization of data sets, ML plays an important role and changing the world of digitization. Class imbalance is the major issue in ML-based systems which is reduced the performance of the systems. The healthcare domain is majorly suffered due to class imbalance. To address the class imbalance issue in healthcare, the proposed study focused on the structure and feature of the data set. The data set is preprocessed using two techniques, traditional preprocessing and using feature extraction technique principle component analysis (PCA). The preprocessed data set is evaluated with various standard ML algorithms (decision tree, support vector machine, neural network analysis, etc.).The evaluation of the data set has been recording accuracy and precision values of unbalanced to balanced data set for both techniques. Finally, results are compared for both techniques and conclude that the data set preprocessed using PCA is performed better than traditional preprocessed data and also handle class imbalance for ML-based systems." @default.
- W4285260944 created "2022-07-14" @default.
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- W4285260944 date "2022-01-01" @default.
- W4285260944 modified "2023-09-29" @default.
- W4285260944 title "Comparative Analysis of Machine Learning Algorithms for Imbalance Data Set Using Principle Component Analysis" @default.
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- W4285260944 doi "https://doi.org/10.1007/978-981-16-9650-3_8" @default.
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