Gene Classification using Effective Random Forest Bootstrap Technique for Predicting the Gene Abnormalities
A. Immaculate Mercy1, M. Chidambaram2
1A.Immaculate Mercy*, Research Scholar ,Department of Computer Science, A.V.V.M Sri Pushpam College, Poondi, Thanjavur, India.
2M.Chidambaram PG and Research ,Department of Computer Science, Rajah Serfoji Govt. Arts College, Thanjavur, India.
Manuscript received on 5 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 2516-2525 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4967098319/2019©BEIESP | DOI: 10.35940/ijrte.C4967.098319
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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: Gene classification is an increasing concern in the field of medicine for identifying various diseases at earlier stages. This work aims to specifically predict the abnormalities in human chromosome-17 by means of effective random forest bootstrap classification. The homo-sapiens dataset is initially preprocessed to remove the unwanted data. The enhanced data undergoes training phase where the appropriate and relevant features are selected by wrapper and filter methods. Based on the feature priorities, decision trees are formulated using random forest technique. The statistical quantities are estimated from the samples and a bootstrap sampling is designated. The effective bootstrap technique classifies the gene abnormalities in chromosome-17. The performance metrics are evaluated and the classification accuracy value is compared with the values of existing algorithms. From the experimental results, it is proved that the proposed method is highly accurate than the conventional methods.
Keywords: Genes, Gene Classification, Gene Properties, Random Forest Bootstrap Technique.
Scope of the Article: Classification