Abstract
Several FCA-based classification algorithms have been proposed, such as GRAND, LEGAL, GALOIS, RULEARNER, CIBLe, and CLNN & CLNB. These classifiers have been compared to standard classification algorithms such as C4.5, Naïve Bayes or IB1. They have never been compared each other in the same platform, except between LEGAL and CIBLe. Here we compare them together both theoretically and experimentally, and also with the standard machine learning algorithm C4.5. Experimental results are discussed.
Cite
CITATION STYLE
Fu, H., Fu, H., Njiwoua, P., & Mephu Nguifo, E. (2004). A comparative study of FCA-based supervised classification algorithms. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2961, pp. 313–320). Springer Verlag. https://doi.org/10.1007/978-3-540-24651-0_26
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
