Diseases Classification with Genetic Algorithm for Support Vector Machine using Hadoop

Authors

  • Shrikant P. Akarte
  • G. R. Bamnote

Abstract

This Paper proposes classification method based genetic algorithm (GA), for many real imbalanced data sets, which has a very small number of different objects and a large number of certain type objects. Support vector machine (SVM) which is a normal classification method, dose not work for skewed statistics sets. Mainly this work is focused on classification of medical disease by the combining hereditary algorithm and uphold vector machines (SVM) which is a feature selection technique higher performance, SVM is best as compared to conventional learning steps within applications. SVM is relatively a novel classification technique. To increase the overall performance of SVM, we use combination of GA and SVM. The proposed method has better classification accuracy related to further admired classification algorithms for several skewed data sets.

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Published

2020-02-07

Issue

Section

Articles