A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT)

This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our approach supports multi-label learning to predict multiple class labels simultaneously, thereby enhancing the model’s capacity to capture complex r...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 12; no. 18; p. 2825
Main Authors: Ghasemkhani, Bita, Balbal, Kadriye Filiz, Birant, Derya
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2024
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
Cybersecurity
Datasets
Decision trees
Disease
Labels
logistic model tree
Logistic regression
Logit models
Machine learning
Mathematical research
mathematics
Medical research
multi-class multi-label dataset
multi-label learning
Neural networks
Performance prediction
Prediction theory
Recommender systems
Regression analysis
Regression models
State-of-the-art reviews
Task complexity
Text categorization
Trees (Graph theory)
Turkey
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary:This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our approach supports multi-label learning to predict multiple class labels simultaneously, thereby enhancing the model’s capacity to capture complex relationships within the data. The primary goal is to improve the accuracy of classification tasks involving multiple classes and labels. MMLMT integrates the logistic regression (LR) and decision tree (DT) algorithms, yielding interpretable models with high predictive performance. By combining the strengths of LR and DT, our method offers a flexible and powerful framework for handling multi-class multi-label data. Extensive experiments demonstrated the effectiveness of MMLMT across a range of well-known datasets with an average accuracy of 85.90%. Furthermore, our method achieved an average of 9.87% improvement compared to the results of state-of-the-art studies in the literature. These results highlight MMLMT’s potential as a valuable approach to multi-label learning.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12182825