Abstract
The curse of dimensionality is a well-known problem in data classification. In this paper, the Center-Based Sampling (CBS) theory is utilized to develop a new variant of Particle Swarm Optimization (PSO), dubbed CBS-PSO, capable of dealing with the curse of dimensionality problem in text classification. More specifically, the CBS is exploited to equip PSO with two specialized mechanisms to attract the search toward the center region of the search space. The first mechanism estimates the coordinates of the center point of the search space using Rocchio Algorithm (RA), whereas the second mechanism uses the RA-based estimation to generate informed particles, located at the center region, and incorporate them in the swarm to gradually attract the search for the optimal classifiers toward this promising region. The performance of the CBS-PSO is evaluated against three Machine Learning (ML) approaches on three classification tasks of textual datasets from UC Irvine ML repository. The results indicate that the CBS-PSO can be regarded as a very competitive and promising text classifier with much space for improvement.
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Yahya, A.A., Asiri, Y., Alattab, A.A. (2021). Utilizing Center-Based Sampling Theory to Enhance Particle Swarm Classification of Textual Data. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_37
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