Active learning has been a hot topic because labeled data are useful, however expensive. Many existing approaches are based on decision trees, Naïve Bayes algorithms, etc. In this paper, we propose a representative-based active learning algorithm with max-min distance. Our algorithm has two techniques interacting with each other. One is the representative-based classification inspired by covering-based neighborhood rough sets. The other is critical instance selection with max-min distance. Experimental results on six UCI datasets indicate that, with the same number of labeled instances, our algorithm is comparable with or better than the ID3, C4.5 and Naïve Bayes algorithms.
CITATION STYLE
Liu, F. L., Min, F., Wen, L. Y., & Wang, H. J. (2016). Representative-based active learning with max-min distance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9920 LNAI, pp. 365–375). Springer Verlag. https://doi.org/10.1007/978-3-319-47160-0_33
Mendeley helps you to discover research relevant for your work.