<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.</p>
</abstract>

Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.

Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation"}

Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy...

Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Pubblicato online: 23 giu 2022

Pagine: 50 - 65

Ricevuto: 14 mar 2021

Accettato: 21 apr 2022

DOI: https://doi.org/10.2478/cait-2022-0016

Parole chiave
Decision tree, virtual merging node, weak nodes, nodes similarity, data augmentation

© 2022 Youness Manzali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Pubblicato online: 23 giu 2022

Pagine: 50 - 65

Ricevuto: 14 mar 2021

Accettato: 21 apr 2022

DOI: https://doi.org/10.2478/cait-2022-0016

Parole chiaveDecision tree, virtual merging node, weak nodes, nodes similarity, data augmentation

© 2022 Youness Manzali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Decision tree, virtual merging node, weak nodes, nodes similarity, data augmentation