Abstract
Naive Bayes is an efficient and effective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly separable functions in the binary domain. We give necessary and sufficient conditions on linearly separable functions in the binary domain to be learnable by Naive Bayes under uniform representation. We then show that the learnability (and error rates) of Naive Bayes can be affected dramatically by sampling distributions. Our results help us to gain a much deeper understanding of this seemingly simple, yet powerful learning algorithm.
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References
Domingos, P., & Pazzani, M. (1997): Beyond independence: Conditions of the optimality of the simple bayesian classifier. Machine Learning, 29, 103–130.
Dougherty, J., Kohavi, R., & Sahami, M. (1995): Supervised and unsupervised discretization of Continuous Features. In Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–202. Morgan Kaufmann.
Duda, R. O., & Hart, P. E. (1973): Pattern classification and scene analysis. A Wiley Interscience Publication.
Kononenko, I. (1990): Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition. In Wielinga, B. (Ed.), Current Trends in Knowledge Acquisition. IOS Press.
Langley, P., Iba, W., & Thomas, K. (1992). An analysis of Bayesian classifier. In proceedings of the Tenth National Conference of Artificial Intelligence, pp. 223–228. AAAI Press.
Pazzani, M., Muramatsu, J., & Billsus, B. (1996). Syskill & webert: Identifying interesting websites. In Proceedings of the Thirteenth National Conference of Artificial Intelligence. pp. 54–62. AAAI Press.
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann: San Mateo, CA.
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Zhang, H., Ling, C.X., Zhao, Z. (2000). The Learnability of Naive Bayes. In: Hamilton, H.J. (eds) Advances in Artificial Intelligence. Canadian AI 2000. Lecture Notes in Computer Science(), vol 1822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45486-1_37
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DOI: https://doi.org/10.1007/3-540-45486-1_37
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