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- W4237577690 abstract "The analization of cancer data and normal data for the predication of somatic mu-tation occurrences in the data set plays an important role and several challenges persist in de-tectingsomatic mutations which leads to complexity of handling large volumes of data in classifi-cation with good accuracy. In many situations the dataset may consist of redundant and less significant features and there is a need to remove insignificant features in order to improve the performance of classification. Feature selection techniques are useful for dimensionality reduction purpose. PCA is one type of feature selection technique to identify significant attributes and is adopted in this paper. A novel technique, PCA based regression decision tree is proposed for classification of somatic mutations data in this paper.The performance analysis of this clas-sification process for the detection of somatic mutation is compared with existing algorithms and satisfactory results are obtained with the proposed model." @default.
- W4237577690 created "2022-05-12" @default.
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- W4237577690 date "2019-11-22" @default.
- W4237577690 modified "2023-09-26" @default.
- W4237577690 title "PCA based Regression Decision Tree Classification for Somatic Mutations." @default.
- W4237577690 doi "https://doi.org/10.35940/ijeat.f1181.0986s319" @default.
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