Classifying Incomplete Vectors using Decision Trees

Bhekisipho Twala, Raj Pillay, Ramapulana Nkoana

2020

Abstract

An attempt is made to address the problem of classifying incomplete vectors using decision trees. The essence of the approach is the proposal that in supervised learning classification of incomplete vectors can be improved in probabilistic terms. This approach, which is based on the a priori probability of each value determined from the instances at that node of the tree that has specified values, first exploits the total probability and Bayes’ theorems and then the probit and logit model probabilities. The proposed approach (developed in three versions) is evaluated using 21 machine learning datasets from its effect or tolerance of incomplete test data. Experimental results are reported, showing the effectiveness of the proposed approach in comparison with multiple imputation and fractioning of instances strategy.

Paper Citation

in Harvard Style

Twala B., Pillay R. and Nkoana R. (2020). Classifying Incomplete Vectors using Decision Trees. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA; ISBN 978-989-758-475-6, SciTePress, pages 455-463. DOI: 10.5220/0010146304550463

in Bibtex Style

@conference{ncta20,
author={Bhekisipho Twala and Raj Pillay and Ramapulana Nkoana},
title={Classifying Incomplete Vectors using Decision Trees},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA},
year={2020},
pages={455-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010146304550463},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA
TI - Classifying Incomplete Vectors using Decision Trees
SN - 978-989-758-475-6
AU - Twala B.
AU - Pillay R.
AU - Nkoana R.
PY - 2020
SP - 455
EP - 463
DO - 10.5220/0010146304550463
PB - SciTePress