Compound Feature Generation And Boosting Model For Cancer Gene Classification

Compound Feature Generation And Boosting Model For Cancer Gene Classification

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-10
Year of Publication : 2020
Authors : S. Jafar Ali Ibrahim, A. Mohamed Affir, M. Thangamani, S. Nallusamy
DOI :  10.14445/22315381/IJETT-V68I10P208

Citation 

MLA Style: S. Jafar Ali Ibrahim, A. Mohamed Affir, M. Thangamani, S. Nallusamy  "Compound Feature Generation And Boosting Model For Cancer Gene Classification" International Journal of Engineering Trends and Technology 68.10(2020):48-51. 

APA Style:S. Jafar Ali Ibrahim, A. Mohamed Affir, M. Thangamani, S. Nallusamy. Compound Feature Generation And Boosting Model For Cancer Gene Classification  International Journal of Engineering Trends and Technology, 68(10),48-51.

Abstract
The huge-data processing applications are conducted utilizing data mining or deep learning approaches. In data processing and deep learning systems, computational complexity is the key problem. High dimensional data analysis requires immense computing time and computer capital. For improved visuality, optimization of data, elimination of noise and comprehensible factors and generalization, dimensionality restriction methods are implemented. The dimensionality reduction activities monitor the data output. In the high dimensional data world, feature selection models are implemented to minimize complexity. Throughout the potential selection process, sub-set filtering with significance element is considered. In the function selection process, quantitative techniques are implemented. The poor results of the T-test configuration are found. F-test models disable the unnecessary functions. To test the apps, Q-statistics activities are added. For the practical enhancement cycle, the booster algorithm is used. For the classification method, the Naïve Bayes algorithm is used. Dynamic characteristics are identified with the filtering methods of the applications. The retrieval of characteristics is implemented in the microscope data values to catch complex properties. The method for integrating feature discovery with abstraction is added to the compound object creation. Many percentage-based attribute associations are introduced for app incorporation. The boosting approach is combined with the production of compound functions. The classification is performed using the algorithm Naïve Bayes with function values produced.

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Keywords
High Dimensional Data Classification, Feature Selection, Feature Extraction, Feature Generation and Naïve Bayesian Classifier