The Significance of Utilizing the dependencies among Labels in Multi Label Classification

Multi-Label Classification (MLC) is a general type of classification that attracted scholars in the last few years. It imposes a high challenge since the problem search space of MLC is very large and follows an exponential function of growth. Moreover, the accuracy of classification in MLC is still...

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Bibliographic Details
Published in2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) pp. 1 - 5
Main Authors Alzoubi, Haneen, Alzyoud, Mazen, Alazaidah, Raed, Al-shanableh, Najah, Khafajah, Hayel, Almatarneh, Sattam
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.12.2023
Subjects
Accuracy
Artificial intelligence
Classification
data mining
Dependencies among labels
machine learning
Measurement
multi-label classification
Search problems
Task analysis
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Summary:Multi-Label Classification (MLC) is a general type of classification that attracted scholars in the last few years. It imposes a high challenge since the problem search space of MLC is very large and follows an exponential function of growth. Moreover, the accuracy of classification in MLC is still very low when compared to the accuracy of Single Label Classification (SLC). Consequently, many scholars and researchers proposed to utilize and exploit the dependencies among class labels in to minimize the size of the problem search space of MLC, and hence, improve the accuracy of the classification task. Unfortunately, very few studies address this issue and attempt to discover the benefits of utilizing and exploiting the dependencies among labels in the domain of MLC. Therefore, this research attempts to identify the significance of discovering and utilizing these dependencies, with respect to three evaluation metrics designed specifically for MLC, and considering four different multi label datasets. The results revealed the clear significance of discovering and utilizing high order dependencies among labels in MLC, especially with high cardinality datasets.
DOI:10.1109/EICEEAI60672.2023.10590280