PCA based Regression Decision Tree Classification for Somatic Mutations
Anuradha Chokka1, K Sandhya Rani2
1Anuradha Chokka, Research Scholar, Department of Computer Science, Sri Padmavathi Mahila Visvavidyalayam, Tirupati (Andhra Pradesh), India.
2Dr. K Sandhya Rani, Professor, Department of Computer Science, Sri Padmavathi Mahila Visvavidyalayam, Tirupati (Andhra Pradesh), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1095-1102 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11810986S319/19©BEIESP | DOI: 10.35940/ijeat.F1181.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 detectingsomatic 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.
Keywords: Somatic Mutations, Feature Selection, Regression Based Decision Trees (RDT).
Scope of the Article: Classification