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FUDT: A Fuzzy Uncertain Decision Tree Algorithm for Classification of Uncertain Data

  • Research Article - Computer Engineering and Computer Science
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Abstract

The classifications of uncertain data turned into one of the dreary procedures in the data mining domain. The uncertain data have tuples with distinctive probability distribution, which helps to find similar class of tuples. When we consider an uncertain data, the feature vector will not be a single valued but a function. In this paper, we proposed fuzzy entropy and similarity measure to characterize the uncertain data through binary decision tree algorithm. Fuzzy entropy is used to find the best split point for the decision tree to handle the uncertain data. Similarity measure is used to make the better decision for the uncertain data with high accuracy. Initially, fuzzy entropy for each feature vector is calculated to select the best feature vector. Then, best split is selected from the selected feature vector. With the help of trained uncertain data, the binary tree starts to grow. Once the split point is selected, then the constructed decision tree is evaluated by the testing phase of uncertain data. The testing data are subjected to the trained decision tree to obtain the classified data. The experimental analyses are made to evaluate the performance of the proposed FUDT approach. Proposed FUDT algorithm is compared with the existing classification algorithm UDT in terms of accuracy and running time. The experimental analysis finalizes that our FUDT algorithm outperforms the existing UDT algorithm.

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Authors and Affiliations

  1. Department of Master of Computer Applications, The Kavery Engineering College, Mecheri, Salem, 636 453, India

    S. Meenakshi

  2. The Kavery Engineering College, Mecheri, Salem, India

    V. Venkatachalam

Authors
  1. S. Meenakshi
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  2. V. Venkatachalam
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Correspondence to S. Meenakshi.

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Meenakshi, S., Venkatachalam, V. FUDT: A Fuzzy Uncertain Decision Tree Algorithm for Classification of Uncertain Data. Arab J Sci Eng 40, 3187–3196 (2015). https://doi.org/10.1007/s13369-015-1800-0

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  • DOI: https://doi.org/10.1007/s13369-015-1800-0

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