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

<p>The speed at which the data is generated, processed and stored to meet the demands of our lives today requires new technologies for handling and using this amount of data. Research on the effective usage of this data suggests that data analysis can contribute to international development, by improving decision-making, in health care, economic, and human resource development. Using artificial intelligence helps in discovering the important features of the data and to use it in classifying known data or in predicting the state of unseen data. In this paper, we propose a hybrid model that combines between Decision Tree algorithm and the Naïve Bayes algorithm in linear functions to improve the performance of a single classifier. Our algorithm is tested for three features and four features on binary data only. The simulation results indicate that our proposed algorithm outperforms the two algorithms tested separately on the same data in terms of accuracy which refers to the number of cases predicted correctly.</p>
</abstract>

The speed at which the data is generated, processed and stored to meet the demands of our lives today requires new technologies for handling and using this amount of data. Research on the effective usage of this data suggests that data analysis can contribute to international development, by improving decision-making, in health care, economic, and human resource development. Using artificial intelligence helps in discovering the important features of the data and to use it in classifying known data or in predicting the state of unseen data. In this paper, we propose a hybrid model that combines between Decision Tree algorithm and the Naïve Bayes algorithm in linear functions to improve the performance of a single classifier. Our algorithm is tested for three features and four features on binary data only. The simulation results indicate that our proposed algorithm outperforms the two algorithms tested separately on the same data in terms of accuracy which refers to the number of cases predicted correctly.

Karnauph Classifier: A Hybrid Mathematical Model for Data Classification

Jadara University, Faculty of Science and Information Technology

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Karnauph Classifier: A Hybrid Mathematical Model for Data Classification"}

The speed at which the data is generated, processed and stored to meet the demands of our lives today requires new technologies for handling and using this...

Karnauph Classifier: A Hybrid Mathematical Model for Data Classification

Pubblicato online: 16 giu 2023

Pagine: 2333 - 2344

Ricevuto: 13 ago 2022

Accettato: 21 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00414

Parole chiave
Hybrid mathematical model, big data analysis, linear regression model, posterior probability, entropy reduction, ensemble learning

© 2023 Arwa Zabian, published by Sciendo

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

Karnauph Classifier: A Hybrid Mathematical Model for Data Classification

Pubblicato online: 16 giu 2023

Pagine: 2333 - 2344

Ricevuto: 13 ago 2022

Accettato: 21 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00414

Parole chiaveHybrid mathematical model, big data analysis, linear regression model, posterior probability, entropy reduction, ensemble learning

© 2023 Arwa Zabian, published by Sciendo

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

Parole chiave
Hybrid mathematical model, big data analysis, linear regression model, posterior probability, entropy reduction, ensemble learning