Abstract
The real-world dataset such as defects in products, credit card frauds, disease diagnosis and natural calamity occurrence is large and often imbalanced. In this paper, a genetic algorithm (GA)-based error classification for imbalanced dataset is proposed. Conventionally, principle component analysis (PCA) was applied for dataset processing and error identification. The approach produced a binary form result for errors present in a dataset. In imbalanced dataset, it is important to determine the location and error percentage in dataset. This is achieved by GA where the dataset is selected at random and compared with the original dataset for error location identification. Furthermore, the sample patch size selected at different levels makes it easier to process different dataset size. The proposed GA detects error location and increases processing time of imbalanced dataset.
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Babu, M.C., Pushpa, S. (2020). Genetic Algorithm-Based PCA Classification for Imbalanced Dataset. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (eds) Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_59
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DOI: https://doi.org/10.1007/978-981-15-2780-7_59
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