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Cluster Computing

, Volume 22, Supplement 6, pp 15073–15080 | Cite as

Multi level incremental influence measure based classification of medical data for improved classification

  • K. AnanthajothiEmail author
  • M. Subramaniam
Article
  • 62 Downloads

Abstract

The problem of classification in medical data has been well studied and there exist number of solution for the classification of medical using different measures and methods. However, the methods still suffer to achieve higher performance in classification accuracy. Towards the development of classification performance, the Multi Level Incremental Influence Measure (MLIIM) based classification algorithm is presented in this paper. The method preprocess the input data set to fix the noise issue by removing the incomplete data. In the second stage, the method estimates the influence measure in multiple levels at iterative manner. Finally, the method estimates, class influence weight (CIW) for different classes. Based on the computed class influence weight, an target class is selected to assign label to the data point. The proposed algorithm produces efficient classification and increases the classification accuracy.

Keywords

Classification Classification accuracy Medical data Influence measure MLIIM 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of CSEMisrimal Navajee Munoth Jain Engineering CollegeChennaiIndia
  2. 2.S.A. Engineering CollegeChennaiIndia

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