Expectation of Radar Returns from Ionosphere Using Decision Tree Technique

  • G. Ameer Basha
  • K. Lakshmana Gupta
  • K. RamakrishnaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)


In this paper, the prediction of Decision Tree classification is assessed using two property attribute selection choice measures for the ionosphere dataset. Decision Tree utilizes isolate and vanquish system for the essential learning procedure. From the outcome investigation, we can reason that the execution of Decision Tree classification depends on the characteristic attribute selection choice measures. Decision Tree is valuable since development of choice tree classifiers does not require any area learning. The primary goal is to manufacture a proficient expectation demonstrate for ionosphere radar comes back with high exactness.


Decision tree Classification Ionosphere Entropy and Gini 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • G. Ameer Basha
    • 1
  • K. Lakshmana Gupta
    • 1
  • K. Ramakrishna
    • 1
    Email author
  1. 1.Department of PhysicsSilver Jubilee Government CollegeKurnoolIndia

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