Open AccessPCA based Regression Decision Tree Classification for Somatic Mutations
Author(s) -
Anuradha Chokka,
K. Sandhya Rani
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1181.0986s319
Subject(s) - decision tree , feature selection , computer science , dimensionality reduction , data mining , artificial intelligence , regression , curse of dimensionality , pattern recognition (psychology) , feature (linguistics) , selection (genetic algorithm) , mutation , tree (set theory) , decision tree learning , machine learning , mathematics , statistics , biology , genetics , mathematical analysis , linguistics , philosophy , gene
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.