Classification by majority voting in feature partitions

Setiapraja, Hari; Murty, M. Narasimha; Saravanan, R.

doi:10.1504/ijids.2016.076509

Cited by 1 publication

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“…The superiority of the method is evaluated with TarBase database (version 3.0) with 10-fold cross validation technique and achieves 93.9% classification accuracy [9]. Privacy-Preserving for ID3 decision tree proposed using Vertical partitioning technique [10] similarly Hari seetha et.al discussed a vertical partitioning approach using SVM classifier where features of the dataset are divided based on the mutual exclusive property [11]. Similarly a non-sequential vertical partitioning method proposed to improve both stability and classification rate of the decision tree [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

Nutheti¹,

Hasyagar²,

Shettar³

et al. 2020

IJMSC

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Decision tree is a known classification technique in machine learning. It is easy to understand and interpret and widely used in known real world applications. Decision tree (DT) faces several challenges such as class imbalance, overfitting and curse of dimensionality. Current study addresses curse of dimensionality problem using partitioning technique. It uses partitioning technique, where features are divided into multiple sets and assigned into each block based on mutual exclusive property. It uses Genetic algorithm to select the features and assign the features into each block based on the ferrer diagram to build multiple CART decision tree. Majority voting technique used to combine the predicted class from the each classifier and produce the major class as output. The novelty of the method is evaluated with 4 datasets from UCI repository and shows approximately 9%, 3% and 5% improvement as compared with CART, Bagging and Adaboost techniques.

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