Classification of Gene Expression Data Set using Support Vectors Machine with RBF Kernel
BM Ramachandro1, Ravi Bhramaramba2
1M Ramachandro, Department of Computer Science & Engg, GMR Institute of Technology, Andhra Pradesh
2Ravi Bhramaramba, Department of Information Technology, GITAM, Andhra Pradesh
Manuscript received on 11 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 2907-2913 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2463078219/19©BEIESP | DOI: 10.35940/ijrteB2463.078219
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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 huge amount of data being generated by different organizations and its underlying advantages in multiple fields like decision making, data security, research purposes have made data classification a very important and mandatory process now-a-days. Data Classification is the process of grouping data of similar characteristics into categories. Classification can be done based on the output we are looking forward to. Hence it is considered very useful. Classifying data allows us to predict the nature of future data-sets and discover useful patterns among them. This project aims at classifying gene data sets. Gene data sets are the information collected from a set of genes put to a specific test. It can be used for medical research purposes; by studying the pattern in the datasets allows us to predict the kind of genes that are more vulnerable to a particular disease there by allowing us to prevent the manifestation of the disease right at its beginning, just as they say, prevention is better than cure. In this paper, such classification is effort using a supervised machine learning algorithm – SVM (Support Vector Machine). There are many algorithms in existence to perform classification but this algorithm has its own lead over the others. It is capable of both classification and regression. It works well with structured, semi-structured and unstructured data too. It contains a kernel function which when used appropriately can solve any complex problem. The summary of this project is, taking gene data sets as input and obtaining classified clusters as output.
Keywords: Data Classification, Gene expression, Support Vector Machine (SVM), Machine Learning, Supervised Algorithms
Scope of the Article: Classification