A Novel Prototype Decision Tree Method Using Sampling Strategy

  • Bhanu Prakash Battula
  • Debnath Bhattacharyya
  • C. V. P. R. Prasad
  • Tai-hoon KimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)


Data Mining is a popular knowledge discovery technique. In data mining decision trees are of the simple and powerful decision making models. One of the limitations in decision trees is towards the data source which they tackle. If data sources which are given as input to decision tree are of imbalance nature then the efficiency of decision tree drops drastically, we propose a decision tree structure which mimics human learning by performing balance of data source to some extent. In this paper, we propose a novel method based on sampling strategy. Extensive experiments, using C4.5 decision tree as base classifier, show that the performance measures of our method is comparable to state-of-the-art methods.


Knowledge discovery Data mining Classification Decision trees Sampling strategy 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bhanu Prakash Battula
    • 1
  • Debnath Bhattacharyya
    • 2
  • C. V. P. R. Prasad
    • 3
  • Tai-hoon Kim
    • 4
    Email author
  1. 1.Department of CSEVignan CollegeGunturIndia
  2. 2.Department of Computer Science and EngineeringVignan’s Institute of Information TechnologyVisakhapatnamIndia
  3. 3.Research ScholarAcharya Nagarjuna UniversityGunturIndia
  4. 4.Department of Convergence SecuritySungshin Women’s UniversitySeoulKorea

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